1
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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2
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Ponce-Alvarez A, Deco G. The Hopf whole-brain model and its linear approximation. Sci Rep 2024; 14:2615. [PMID: 38297071 PMCID: PMC10831083 DOI: 10.1038/s41598-024-53105-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/27/2024] [Indexed: 02/02/2024] Open
Abstract
Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.
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Affiliation(s)
- Adrián Ponce-Alvarez
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain
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3
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Yang L, Lu J, Li D, Xiang J, Yan T, Sun J, Wang B. Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models. Brain Sci 2023; 13:1133. [PMID: 37626490 PMCID: PMC10452161 DOI: 10.3390/brainsci13081133] [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: 06/27/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.
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Affiliation(s)
- Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China;
| | - Jie Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
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4
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Ghosh S, Raj A, Nagarajan SS. A joint subspace mapping between structural and functional brain connectomes. Neuroimage 2023; 272:119975. [PMID: 36870432 PMCID: PMC11244732 DOI: 10.1016/j.neuroimage.2023.119975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
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Affiliation(s)
- Sanjay Ghosh
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, 94143, California, USA.
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, 94143, California, USA.
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, 94143, California, USA.
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5
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Piccinini J, Deco G, Kringelbach M, Laufs H, Sanz Perl Y, Tagliazucchi E. Data-driven discovery of canonical large-scale brain dynamics. Cereb Cortex Commun 2022; 3:tgac045. [PMID: 36479448 PMCID: PMC9721525 DOI: 10.1093/texcom/tgac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 09/23/2023] Open
Abstract
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
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Affiliation(s)
- Juan Piccinini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra Plaça de la Mercè, 10-12, 08002 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Passeig Lluís Companys 23 08010 Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- School of Psychological Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia
| | - Morten Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Building 1710, Universitetsbyen 3, 8000 Aarhus C, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, University of Oxford, 7 Stoke Pl, Headington, Oxford OX3 9BX, United Kingdom
| | - Helmut Laufs
- Neurology Department, Schleswig-Holstein University Hospital, University of Kiel, Haus D, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284 Victoria, Buenos Aires, Argentina
- Paris Brain Institute (ICM), Hôpital Pitié, 47 Bd de l'Hôpital, 75013 Paris, France
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Diagonal las Torres 2640, Peñalolén Av. Presidente Errázuriz 3485, Las Condes, Santiago, Chile
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6
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Reliability and subject specificity of personalized whole-brain dynamical models. Neuroimage 2022; 257:119321. [PMID: 35580807 DOI: 10.1016/j.neuroimage.2022.119321] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.
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7
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Mariani B, Nicoletti G, Bisio M, Maschietto M, Vassanelli S, Suweis S. Disentangling the critical signatures of neural activity. Sci Rep 2022; 12:10770. [PMID: 35750684 PMCID: PMC9232560 DOI: 10.1038/s41598-022-13686-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/26/2022] [Indexed: 11/09/2022] Open
Abstract
The critical brain hypothesis has emerged as an attractive framework to understand neuronal activity, but it is still widely debated. In this work, we analyze data from a multi-electrodes array in the rat's cortex and we find that power-law neuronal avalanches satisfying the crackling-noise relation coexist with spatial correlations that display typical features of critical systems. In order to shed a light on the underlying mechanisms at the origin of these signatures of criticality, we introduce a paradigmatic framework with a common stochastic modulation and pairwise linear interactions inferred from our data. We show that in such models power-law avalanches that satisfy the crackling-noise relation emerge as a consequence of the extrinsic modulation, whereas scale-free correlations are solely determined by internal interactions. Moreover, this disentangling is fully captured by the mutual information in the system. Finally, we show that analogous power-law avalanches are found in more realistic models of neural activity as well, suggesting that extrinsic modulation might be a broad mechanism for their generation.
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Affiliation(s)
| | - Giorgio Nicoletti
- Department of Physics and Astronomy "G. Galilei", INFN, University of Padova, Padua, Italy
| | - Marta Bisio
- Department of Biomedical Sciences, University of Padova, Padua, Italy
| | - Marta Maschietto
- Department of Biomedical Sciences, University of Padova, Padua, Italy
| | - Stefano Vassanelli
- Padova Neuroscience Center, University of Padova, Padua, Italy.
- Department of Biomedical Sciences, University of Padova, Padua, Italy.
| | - Samir Suweis
- Department of Physics and Astronomy "G. Galilei", INFN, University of Padova, Padua, Italy.
- Padova Neuroscience Center, University of Padova, Padua, Italy.
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8
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Deslauriers-Gauthier S, Zucchelli M, Laghrissi H, Deriche R. A Riemannian Revisiting of Structure-Function Mapping Based on Eigenmodes. FRONTIERS IN NEUROIMAGING 2022; 1:850266. [PMID: 37555180 PMCID: PMC10406294 DOI: 10.3389/fnimg.2022.850266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/23/2022] [Indexed: 08/10/2023]
Abstract
Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure-function mapping. Specifically, we revisit previously proposed structure-function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so-called glass ceiling on the performance of mappings based on eigenmodes.
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9
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Cummings JA, Sipes B, Mathalon DH, Raj A. Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping. Front Neurosci 2022; 16:810111. [PMID: 35368264 PMCID: PMC8964629 DOI: 10.3389/fnins.2022.810111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.
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Affiliation(s)
- Jennifer A. Cummings
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin Sipes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Daniel H. Mathalon
- San Francisco VA Medical Center, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Ashish Raj
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Ashish Raj
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10
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An S, Fousek J, Kiss ZHT, Cortese F, van der Wijk G, McAusland LB, Ramasubbu R, Jirsa VK, Protzner AB. High-resolution Virtual Brain Modeling Personalizes Deep Brain Stimulation for Treatment-Resistant Depression: Spatiotemporal Response Characteristics Following Stimulation of Neural Fiber Pathways. Neuroimage 2021; 249:118848. [PMID: 34954330 DOI: 10.1016/j.neuroimage.2021.118848] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/25/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023] Open
Abstract
Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100 000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.
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Affiliation(s)
- Sora An
- Department of Communication Disorders, Ewha Womans University, 03760, Seoul, Republic of Korea.
| | - Jan Fousek
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France
| | - Zelma H T Kiss
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Filomeno Cortese
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Seaman Family MR Centre, Foothills Medical Centre, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Gwen van der Wijk
- Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Laina Beth McAusland
- Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Rajamannar Ramasubbu
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France.
| | - Andrea B Protzner
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada.
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11
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Parkes L, Moore TM, Calkins ME, Cieslak M, Roalf DR, Wolf DH, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms. Biol Psychiatry 2021; 90:409-418. [PMID: 34099190 PMCID: PMC8842484 DOI: 10.1016/j.biopsych.2021.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/11/2021] [Accepted: 03/15/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND The psychosis spectrum (PS) is associated with structural dysconnectivity concentrated in transmodal cortex. However, understanding of this pathophysiology has been limited by an overreliance on examining direct interregional connectivity. Using network control theory, we measured variation in both direct and indirect connectivity to a region to gain new insights into the pathophysiology of the PS. METHODS We used psychosis symptom data and structural connectivity in 1068 individuals from the Philadelphia Neurodevelopmental Cohort. Applying a network control theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Using nonlinear regression, we determined the accuracy with which average controllability could predict PS symptoms in out-of-sample testing. We also examined the predictive performance of regional strength, which indexes only direct connections to a region, as well as several graph-theoretic measures of centrality that index indirect connectivity. Finally, we assessed how the prediction performance for PS symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex. RESULTS Average controllability outperformed all other connectivity features at predicting positive PS symptoms and was the only feature to yield above-chance predictive performance. Improved prediction for average controllability was concentrated in transmodal cortex, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections through average controllability is crucial in association cortex. CONCLUSIONS Examining interindividual variation in direct and indirect structural connections to transmodal cortex is crucial for accurate prediction of positive PS symptoms.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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12
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ADHD symptoms map onto noise-driven structure-function decoupling between hub and peripheral brain regions. Mol Psychiatry 2021; 26:4036-4045. [PMID: 31666679 DOI: 10.1038/s41380-019-0554-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 07/18/2019] [Accepted: 08/19/2019] [Indexed: 12/11/2022]
Abstract
Adults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure-function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure-function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure-function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure-function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure-function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure-function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.
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13
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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14
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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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15
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Wein S, Malloni WM, Tomé AM, Frank SM, Henze GI, Wüst S, Greenlee MW, Lang EW. A graph neural network framework for causal inference in brain networks. Sci Rep 2021; 11:8061. [PMID: 33850173 PMCID: PMC8044149 DOI: 10.1038/s41598-021-87411-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/26/2021] [Indexed: 02/02/2023] Open
Abstract
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.
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Affiliation(s)
- S Wein
- CIML, Biophysics, University of Regensburg, 93040, Regensburg, Germany.
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany.
| | - W M Malloni
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - A M Tomé
- IEETA/DETI, Universidade de Aveiro, 3810-193, Aveiro, Portugal
| | - S M Frank
- Department of Cognitive, Linguistic,and Psychological Sciences, Brown University, Providence, RI, 02912, USA
| | - G -I Henze
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - S Wüst
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - M W Greenlee
- Experimental Psychology, University of Regensburg, 93040, Regensburg, Germany
| | - E W Lang
- CIML, Biophysics, University of Regensburg, 93040, Regensburg, Germany
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16
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Naze S, Proix T, Atasoy S, Kozloski JR. Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes. Neuroimage 2021; 224:117364. [PMID: 32947015 PMCID: PMC7779370 DOI: 10.1016/j.neuroimage.2020.117364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/14/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, it has been proposed that the harmonic patterns emerging from the brain's structural connectivity underlie the resting state networks of the human brain. These harmonic patterns, termed connectome harmonics, are estimated as the Laplace eigenfunctions of the combined gray and white matters connectivity matrices and yield a connectome-specific extension of the well-known Fourier basis. However, it remains unclear how topological properties of the combined connectomes constrain the precise shape of the connectome harmonics and their relationships to the resting state networks. Here, we systematically study how alterations of the local and long-range connectivity matrices affect the spatial patterns of connectome harmonics. Specifically, the proportion of local gray matter homogeneous connectivity versus long-range white-matter heterogeneous connectivity is varied by means of weight-based matrix thresholding, distance-based matrix trimming, and several types of matrix randomizations. We demonstrate that the proportion of local gray matter connections plays a crucial role for the emergence of wide-spread, functionally meaningful, and originally published connectome harmonic patterns. This finding is robust for several different cortical surface templates, mesh resolutions, or widths of the local diffusion kernel. Finally, using the connectome harmonic framework, we also provide a proof-of-concept for how targeted structural changes such as the atrophy of inter-hemispheric callosal fibers and gray matter alterations may predict functional deficits associated with neurodegenerative conditions.
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Affiliation(s)
- Sébastien Naze
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA; IBM Research Australia, Melbourne, Victoria, Australia.
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, UK
| | - James R Kozloski
- IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
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17
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Cofré R, Herzog R, Mediano PA, Piccinini J, Rosas FE, Sanz Perl Y, Tagliazucchi E. Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up. Brain Sci 2020; 10:E626. [PMID: 32927678 PMCID: PMC7565030 DOI: 10.3390/brainsci10090626] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/03/2020] [Accepted: 09/07/2020] [Indexed: 01/16/2023] Open
Abstract
The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness.
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Affiliation(s)
- Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2340000, Chile
| | - Rubén Herzog
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso 2360103, Chile;
| | - Pedro A.M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK;
| | - Juan Piccinini
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires C1428EGA, Argentina
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK;
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Yonatan Sanz Perl
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Departamento de Matemáticas y Ciencias, Universidad de San Andrés, Buenos Aires B1644BID, Argentina
| | - Enzo Tagliazucchi
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires C1428EGA, Argentina
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18
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Deslauriers-Gauthier S, Zucchelli M, Frigo M, Deriche R. A unified framework for multimodal structure-function mapping based on eigenmodes. Med Image Anal 2020; 66:101799. [PMID: 32889301 DOI: 10.1016/j.media.2020.101799] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/10/2020] [Accepted: 08/06/2020] [Indexed: 11/28/2022]
Abstract
Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure-function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure-function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure-function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.
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Affiliation(s)
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
| | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, France
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19
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Forrester M, Crofts JJ, Sotiropoulos SN, Coombes S, O'Dea RD. The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Netw Neurosci 2020; 4:467-483. [PMID: 32537537 PMCID: PMC7286301 DOI: 10.1162/netn_a_00130] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/31/2020] [Indexed: 11/07/2022] Open
Abstract
The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure–function issue, treating a system of Jansen–Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure–function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called ‘false bifurcations’ (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states. Patterns of oscillation across the brain arise because of structural connections between brain regions. However, the type of oscillation at a site may also play a contributory role. We focus on an idealised model of a neural mass network, coupled using estimates of structural connections obtained via tractography on Human Connectome Project MRI data. Using a mixture of computational and mathematical techniques, we show that functional connectivity is inherited most strongly from structural connectivity when the network nodes are poised at a Hopf bifurcation. However, beyond the onset of this oscillatory instability a phase-locked network state can undergo a false bifurcation, and structural connectivity only weakly influences functional connectivity. This highlights the important effect that local dynamics can have on large-scale brain states.
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Affiliation(s)
- Michael Forrester
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Jonathan J Crofts
- Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Reuben D O'Dea
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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20
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Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches. Neuroimage 2020; 216:116805. [PMID: 32335264 DOI: 10.1016/j.neuroimage.2020.116805] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/14/2020] [Accepted: 03/31/2020] [Indexed: 12/11/2022] Open
Abstract
Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.
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21
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Novelli L, Atay FM, Jost J, Lizier JT. Deriving pairwise transfer entropy from network structure and motifs. Proc Math Phys Eng Sci 2020; 476:20190779. [PMID: 32398937 PMCID: PMC7209155 DOI: 10.1098/rspa.2019.0779] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 03/24/2020] [Indexed: 11/12/2022] Open
Abstract
Transfer entropy (TE) is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) TE from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in. This relationship is studied using a discrete-time linearly coupled Gaussian model, which allows us to derive the TE for each link from the network topology. It is shown analytically that the dependence on the directed link weight is only a first approximation, valid for weak coupling. More generally, the TE increases with the in-degree of the source and decreases with the in-degree of the target, indicating an asymmetry of information transfer between hubs and low-degree nodes. In addition, the TE is directly proportional to weighted motif counts involving common parents or multiple walks from the source to the target, which are more abundant in networks with a high clustering coefficient than in random networks. Our findings also apply to Granger causality, which is equivalent to TE for Gaussian variables. Moreover, similar empirical results on random Boolean networks suggest that the dependence of the TE on the in-degree extends to nonlinear dynamics.
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Affiliation(s)
- Leonardo Novelli
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
| | - Fatihcan M. Atay
- Department of Mathematics, Bilkent University, 06800 Ankara, Turkey
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany
- Santa Fe Institute for the Sciences of Complexity, Santa Fe, New Mexico 87501, USA
| | - Joseph T. Lizier
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, Australia
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany
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22
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Forrester M, Crofts JJ, Sotiropoulos SN, Coombes S, O'Dea RD. The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Netw Neurosci 2020; 4:467-483. [PMID: 32537537 DOI: 10.1162/netn\_a_00130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/31/2020] [Indexed: 05/21/2023] Open
Abstract
The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure-function issue, treating a system of Jansen-Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure-function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called 'false bifurcations' (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states.
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Affiliation(s)
- Michael Forrester
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Jonathan J Crofts
- Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Reuben D O'Dea
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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23
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Abstract
Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.
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24
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How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes. Neuroimage 2018; 186:211-220. [PMID: 30399418 DOI: 10.1016/j.neuroimage.2018.10.079] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 08/06/2018] [Accepted: 10/29/2018] [Indexed: 01/14/2023] Open
Abstract
Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1-4 Hz) to the high gamma band (40-70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.
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25
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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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26
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O'Neill GC, Tewarie P, Vidaurre D, Liuzzi L, Woolrich MW, Brookes MJ. Dynamics of large-scale electrophysiological networks: A technical review. Neuroimage 2018; 180:559-576. [PMID: 28988134 DOI: 10.1016/j.neuroimage.2017.10.003] [Citation(s) in RCA: 115] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/23/2017] [Accepted: 10/02/2017] [Indexed: 12/12/2022] Open
Abstract
For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
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Tu C, Rocha RP, Corbetta M, Zampieri S, Zorzi M, Suweis S. Warnings and caveats in brain controllability. Neuroimage 2018; 176:83-91. [PMID: 29654874 PMCID: PMC6607911 DOI: 10.1016/j.neuroimage.2018.04.010] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 03/09/2018] [Accepted: 04/06/2018] [Indexed: 12/19/2022] Open
Abstract
A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their "controllability", drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.
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Affiliation(s)
- Chengyi Tu
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Rodrigo P Rocha
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Maurizio Corbetta
- Dipartimento di Neuroscienze, Università di Padova, Padova, Italy; Departments of Neurology, Radiology, Neuroscience, and Bioengineering, Washington University, School of Medicine, St. Louis, USA; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Sandro Zampieri
- Dipartimento di Ingegneria dell'informazione, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Marco Zorzi
- Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy; IRCCS San Camillo Hospital Foundation, Venice, Italy
| | - S Suweis
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy.
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28
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Messé A, Hütt MT, Hilgetag CC. Toward a theory of coactivation patterns in excitable neural networks. PLoS Comput Biol 2018; 14:e1006084. [PMID: 29630592 PMCID: PMC5908206 DOI: 10.1371/journal.pcbi.1006084] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 04/19/2018] [Accepted: 03/11/2018] [Indexed: 11/18/2022] Open
Abstract
The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is of central importance in brain network science. It is an open question, however, how the SC-FC relationship depends on specific topological features of brain networks or the models used for describing neural dynamics. Using a basic but general model of discrete excitable units that follow a susceptible—excited—refractory activity cycle (SER model), we here analyze how the network activity patterns underlying functional connectivity are shaped by the characteristic topological features of the network. We develop an analytical framework for describing the contribution of essential topological elements, such as common inputs and pacemakers, to the coactivation of nodes, and demonstrate the validity of the approach by comparison of the analytical predictions with numerical simulations of various exemplar networks. The present analytic framework may serve as an initial step for the mechanistic understanding of the contributions of brain network topology to brain dynamics. Functional connectivity, as reflected in the statistical dependencies of distributed activity, is widely used to probe the organization of complex systems such as the brain. While this measure has been helpful for characterizing brain states and highlighting alterations of brain dynamics in various diseases, the mechanisms underlying the generation of FC patterns remain poorly understood. One prominent factor shaping FC is the underlying neural network structure. Using a minimalist model of excitation, we investigate how the topology of excitable neural networks contributes to FC. Specifically, we show that FC can be analytically predicted from the way in which the nodes are embedded in the network and how they are related to basic self-organizing units of excitable dynamics, particularly, short pacemaker cycles. These insights are a step towards a mechanistic understanding of the activation patterns of complex neural networks.
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Affiliation(s)
- Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
- * E-mail:
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29
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Surampudi SG, Naik S, Surampudi RB, Jirsa VK, Sharma A, Roy D. Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain. Sci Rep 2018; 8:3265. [PMID: 29459634 PMCID: PMC5818607 DOI: 10.1038/s41598-018-21456-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 01/31/2018] [Indexed: 12/13/2022] Open
Abstract
A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.
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Affiliation(s)
| | - Shruti Naik
- Cognitive Science Lab, IIIT-Hyderabad, Hyderabad, 500032, India
| | - Raju Bapi Surampudi
- Cognitive Science Lab, IIIT-Hyderabad, Hyderabad, 500032, India
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India
| | - Viktor K Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | | | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, Haryana, 122 051, India.
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30
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Bettinardi RG, Deco G, Karlaftis VM, Van Hartevelt TJ, Fernandes HM, Kourtzi Z, Kringelbach ML, Zamora-López G. How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure. CHAOS (WOODBURY, N.Y.) 2017; 27:047409. [PMID: 28456160 DOI: 10.1063/1.4980099] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.
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Affiliation(s)
- R G Bettinardi
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - G Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - V M Karlaftis
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - T J Van Hartevelt
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - H M Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Z Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - M L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - G Zamora-López
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
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31
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Bezgin G, Solodkin A, Bakker R, Ritter P, McIntosh AR. Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains". Hum Brain Mapp 2017; 38:2080-2093. [PMID: 28054725 DOI: 10.1002/hbm.23506] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 11/08/2016] [Accepted: 12/20/2016] [Indexed: 11/09/2022] Open
Abstract
Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080-2093, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Gleb Bezgin
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada, M6A 2E1.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, H3A 2B4
| | - Ana Solodkin
- Department of Neurology, University of California, Irvine, 200 Manchester Avenue, Suite 206, Orange, California
| | - Rembrandt Bakker
- Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, AJ, 6525, the Netherlands.,Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, Jülich, 52425, Germany
| | - Petra Ritter
- Department of Neurology, Charite - University Medicine, Berlin, Germany.,Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany.,Berlin School of Mind and Brain & Mind & Brain Institute, Humboldt University, Berlin, Germany
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ontario, Canada, M6A 2E1.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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32
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Alfaro FJ, Lioutas VA, Pimentel DA, Chung CC, Bedoya F, Yoo WK, Novak V. Cognitive decline in metabolic syndrome is linked to microstructural white matter abnormalities. J Neurol 2016; 263:2505-2514. [PMID: 27730376 DOI: 10.1007/s00415-016-8292-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 09/19/2016] [Accepted: 09/20/2016] [Indexed: 01/21/2023]
Abstract
Subjects with metabolic syndrome (MetS) often show worse cognitive performance compared with the healthy population. We investigated whether microstructural white matter abnormalities are associated with cognitive performance in adults with MetS using diffusion tensor MR imaging. A total of 32 subjects with MetS (age 64.8 ± 7.8, 56.25 % female) and 23 age-, gender-, and education-matched healthy controls completed a battery of neuropsychological tests and diffusion tensor imaging (DTI) at 3-T MRI. Brain global and regional volumes, white matter fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (LD) were calculated. The least-square models adjusted for age, sex, HbA1c, hypertension, body mass index, hyperlipidemia, and white matter hyperintensities were used to evaluate the relationship between cognitive function and DTI. The MetS group had worse performance in verbal fluency (VF) and learning and memory function (total VF: T score (p = 0.01), VF: animals T score (p = 0.0001), Hopkins Verbal Learning Test (HVLT): Total recall T score (p = 0.0001), and HVLT: delayed recall T score (p = 0.002), as compared with controls. In the MetS group, abnormalities in diffusivity measures were associated with worse cognitive performance [VF: animals T score and left post-central gyrus-LD (p = 0.0007, r adj 0.4), R angular gyrus-RD (p = 0.0008, r adj 0.3), L supra-marginal gyrus-RD (p = 0.009, r adj 0.2) after adjusting for age, sex, HbA1c, 24 h mean BP, presence of hyperlipidemia, and global white matter hyperintensities]. Microstructural white matter abnormalities in the MetS group might be the underlying mechanisms of worse verbal learning and memory performance.
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Affiliation(s)
- Freddy J Alfaro
- Department of Neurology, Beth Israel Deaconess Medical Center, 185 Pilgrim Road, Palmer 127, Boston, MA, 02215, USA
| | - Vasileios-Arsenios Lioutas
- Department of Neurology, Beth Israel Deaconess Medical Center, 185 Pilgrim Road, Palmer 127, Boston, MA, 02215, USA
| | - Daniela A Pimentel
- Department of Neurology, Beth Israel Deaconess Medical Center, 185 Pilgrim Road, Palmer 127, Boston, MA, 02215, USA
| | - Chen-Chih Chung
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Francisco Bedoya
- Department of Neurology, Beth Israel Deaconess Medical Center, 185 Pilgrim Road, Palmer 127, Boston, MA, 02215, USA
| | - Woo-Kyoung Yoo
- Department of Physical Medicine and Rehabilitation, Hallym University College of Medicine, Chooncheon, Korea
- Hallym Institute of Translational Genomics and Bioinformatics, Hallym University Sacred Heart Hospital, Anyang, Korea
| | - Vera Novak
- Department of Neurology, Beth Israel Deaconess Medical Center, 185 Pilgrim Road, Palmer 127, Boston, MA, 02215, USA.
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