1
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Li Q, Zhao Y, Hu Y, Liu Y, Wang Y, Zhang Q, Long F, Chen Y, Wang Y, Li H, Poels EMP, Kamperman AM, Sweeney JA, Kuang W, Li F, Gong Q. Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder. EBioMedicine 2024; 106:105255. [PMID: 39032426 PMCID: PMC11324849 DOI: 10.1016/j.ebiom.2024.105255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 06/14/2024] [Accepted: 07/08/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. METHODS Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. FINDINGS Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. INTERPRETATION These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. FUNDING National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).
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Affiliation(s)
- Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Youjin Zhao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yongbo Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yang Liu
- Academy of Mathematics and Systems Science Chinese, Academy of Science, Beijing, China
| | - Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yitian Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Haoran Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Eline M P Poels
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Astrid M Kamperman
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - John A Sweeney
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Department of Psychiatry and Behavioural Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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2
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Stanford W, Mucha PJ, Dayan E. Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks. Commun Biol 2024; 7:701. [PMID: 38849512 PMCID: PMC11161655 DOI: 10.1038/s42003-024-06392-2] [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: 03/20/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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Affiliation(s)
- William Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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3
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Cash RFH, Zalesky A. Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities. Biol Psychiatry 2024; 95:510-522. [PMID: 38040047 DOI: 10.1016/j.biopsych.2023.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023]
Abstract
The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.
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Affiliation(s)
- Robin F H Cash
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
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4
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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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5
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Manjunatha KKH, Baron G, Benozzo D, Silvestri E, Corbetta M, Chiuso A, Bertoldo A, Suweis S, Allegra M. Controlling target brain regions by optimal selection of input nodes. PLoS Comput Biol 2024; 20:e1011274. [PMID: 38215166 PMCID: PMC10810536 DOI: 10.1371/journal.pcbi.1011274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 01/25/2024] [Accepted: 12/04/2023] [Indexed: 01/14/2024] Open
Abstract
The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.
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Affiliation(s)
- Karan Kabbur Hanumanthappa Manjunatha
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, Napoli, Italy
| | - Giorgia Baron
- Information Engineering Department, University of Padova, Padova, Italy
| | - Danilo Benozzo
- Information Engineering Department, University of Padova, Padova, Italy
| | - Erica Silvestri
- Information Engineering Department, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Neuroscience Department, University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Alessandro Chiuso
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Information Engineering Department, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Samir Suweis
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Michele Allegra
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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6
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [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: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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7
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Luppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching S, Sanz Perl Y, Diringer MN, Stevens RD, Sitt JD. Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. Neuroimage 2023; 275:120162. [PMID: 37196986 PMCID: PMC10262065 DOI: 10.1016/j.neuroimage.2023.120162] [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: 01/15/2023] [Revised: 04/16/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023] Open
Abstract
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Portugal
| | - Rodrigo Cofre
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile; Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Abid Y Qureshi
- University of Kansas Medical Center, Kansas City, MO, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Enzo Tagliazucchi
- Departamento de Física (UBA) e Instituto de Fisica de Buenos Aires (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso and Instituto de Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; National Scientific and Technical Research Council (CONICET), Godoy Cruz, CABA 2290, Argentina
| | - Michael N Diringer
- Department of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology, and Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
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8
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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9
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Stanford W, Mucha PJ, Dayan E. Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.528999. [PMID: 36824776 PMCID: PMC9949152 DOI: 10.1101/2023.02.17.528999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we used diffusion MRI data from cognitively intact participants (n=480, ages 40-90) to study age-associated changes in the controllability of structural brain networks, features that could mitigate these changes, and the overall effect on cognitive function. We found age-associated declines in controllability in control hubs and large-scale networks, particularly within the and frontoparietal control and default mode networks. Redundancy, quantified via the assessment of multi-step paths within networks, mitigated the effects of changes in topology on network controllability. Lastly, network controllability, redundancy, and grey matter volume each played important complementary roles in cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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Asp AJ, Chintaluru Y, Hillan S, Lujan JL. Targeted neuroplasticity in spatiotemporally patterned invasive neuromodulation therapies for improving clinical outcomes. Front Neuroinform 2023; 17:1150157. [PMID: 37035718 PMCID: PMC10080034 DOI: 10.3389/fninf.2023.1150157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Affiliation(s)
- Anders J. Asp
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - Yaswanth Chintaluru
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Department of Neurology and Neurosurgery, University of Colorado Anschutz School of Medicine, Aurora, CO, United States
| | - Sydney Hillan
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - J. Luis Lujan
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
- *Correspondence: J. Luis Lujan
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11
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Menardi A, Momi D, Vallesi A, Barabási AL, Towlson EK, Santarnecchi E. Maximizing brain networks engagement via individualized connectome-wide target search. Brain Stimul 2022; 15:1418-1431. [PMID: 36252908 DOI: 10.1016/j.brs.2022.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/29/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation. OBJECTIVE Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes. RESULTS At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states. CONCLUSIONS The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.
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Affiliation(s)
- Arianna Menardi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Davide Momi
- Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio", Chieti, Italy; Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto, Canada
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, AB, Canada; Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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12
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The impact of aging on human brain network target controllability. Brain Struct Funct 2022; 227:3001-3015. [DOI: 10.1007/s00429-022-02584-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/09/2022] [Indexed: 11/27/2022]
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13
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Zhang Y, Ryali S, Cai W, Supekar K, Pasumarthy R, Padmanabhan A, Luna B, Menon V. Developmental maturation of causal signaling hubs in voluntary control of saccades and their functional controllability. Cereb Cortex 2022; 32:4746-4762. [PMID: 35094063 PMCID: PMC9627122 DOI: 10.1093/cercor/bhab514] [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: 08/17/2021] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 02/01/2023] Open
Abstract
The ability to adaptively respond to behaviorally relevant cues in the environment, including voluntary control of automatic but inappropriate responses and deployment of a goal-relevant alternative response, undergoes significant maturation from childhood to adulthood. Importantly, the maturation of voluntary control processes influences the developmental trajectories of several key cognitive domains, including executive function and emotion regulation. Understanding the maturation of voluntary control is therefore of fundamental importance, but little is known about the underlying causal functional circuit mechanisms. Here, we use state-space and control-theoretic modeling to investigate the maturation of causal signaling mechanisms underlying voluntary control over saccades. We demonstrate that directed causal interactions in a canonical saccade network undergo significant maturation between childhood and adulthood. Crucially, we show that the frontal eye field (FEF) is an immature causal signaling hub in children during control over saccades. Using control-theoretic analysis, we then demonstrate that the saccade network is less controllable in children and that greater energy is required to drive FEF dynamics in children compared to adults. Our findings provide novel evidence that strengthening of causal signaling hubs and controllability of FEF are key mechanisms underlying age-related improvements in the ability to plan and execute voluntary control over saccades.
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Affiliation(s)
- Yuan Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Srikanth Ryali
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weidong Cai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ramkrishna Pasumarthy
- Department of Electrical Engineering, Robert Bosch Center of Data Sciences and Artificial Intelligence, Network Systems Learning, Control and Evolution Group, Indian Institute of Technology Madras, Chennai 600036, India
| | - Aarthi Padmanabhan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bea Luna
- Department of Psychiatry and Behavioral Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Wu Tsai Neuroscience Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
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14
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Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions. Commun Biol 2022; 5:394. [PMID: 35484213 PMCID: PMC9050895 DOI: 10.1038/s42003-022-03342-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/06/2022] [Indexed: 02/06/2023] Open
Abstract
Network control theory provides a framework by which neurophysiological dynamics of the brain can be modelled as a function of the structural connectome constructed from diffusion MRI. Average controllability describes the ability of a region to drive the brain to easy-to-reach neurophysiological states whilst modal controllability describes the ability of a region to drive the brain to difficult-to-reach states. In this study, we identify increases in mean average and modal controllability in children with drug-resistant epilepsy compared to healthy controls. Using simulations, we purport that these changes may be a result of increased thalamocortical connectivity. At the node level, we demonstrate decreased modal controllability in the thalamus and posterior cingulate regions. In those undergoing resective surgery, we also demonstrate increased modal controllability of the resected parcels, a finding specific to patients who were rendered seizure free following surgery. Changes in controllability are a manifestation of brain network dysfunction in epilepsy and may be a useful construct to understand the pathophysiology of this archetypical network disease. Understanding the mechanisms underlying these controllability changes may also facilitate the design of network-focussed interventions that seek to normalise network structure and function.
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15
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Kawakita G, Kamiya S, Sasai S, Kitazono J, Oizumi M. Quantifying brain state transition cost via Schrödinger Bridge. Netw Neurosci 2022; 6:118-134. [PMID: 35356194 PMCID: PMC8959122 DOI: 10.1162/netn_a_00213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 10/18/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.
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Affiliation(s)
- Genji Kawakita
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Shunsuke Kamiya
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Shuntaro Sasai
- Araya Inc., Tokyo, Japan
- University of Wisconsin–Madison, Madison, WI, USA
| | - Jun Kitazono
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
| | - Masafumi Oizumi
- Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan
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16
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The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology 2022; 47:90-103. [PMID: 34408276 PMCID: PMC8616903 DOI: 10.1038/s41386-021-01152-w] [Citation(s) in RCA: 167] [Impact Index Per Article: 83.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 01/03/2023]
Abstract
Systems neuroscience approaches with a focus on large-scale brain organization and network analysis are advancing foundational knowledge of how cognitive control processes are implemented in the brain. Over the past decade, technological and computational innovations in the study of brain connectivity have led to advances in our understanding of how brain networks function, inspiring new conceptualizations of the role of prefrontal cortex (PFC) networks in the coordination of cognitive control. In this review, we describe six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control. Implementation of cognitive control in a constantly changing environment depends on the dynamic and flexible organization of PFC networks. In this context, we describe major empirical and theoretical models that have emerged in recent years and describe how their functional architecture and dynamic organization supports flexible cognitive control. We take an overarching view of advances made in the past few decades and consider fundamental issues regarding PFC network function, global brain dynamics, and cognition that still need to be resolved. We conclude by clarifying important future directions for research on cognitive control and their implications for advancing our understanding of PFC networks in brain disorders.
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17
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Jazayeri M, Ostojic S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. Curr Opin Neurobiol 2021; 70:113-120. [PMID: 34537579 PMCID: PMC8688220 DOI: 10.1016/j.conb.2021.08.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/16/2022]
Abstract
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here, we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
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Affiliation(s)
- Mehrdad Jazayeri
- McGovern Institute for Brain Research, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives, INSERM U960, École Normale Supérieure - PSL Research University, 75005, Paris, France.
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18
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Language Tasks and the Network Control Role of the Left Inferior Frontal Gyrus. eNeuro 2021; 8:ENEURO.0382-20.2021. [PMID: 34244340 PMCID: PMC8431826 DOI: 10.1523/eneuro.0382-20.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 11/21/2022] Open
Abstract
Recent work has combined cognitive neuroscience and control theory to make predictions about cognitive control functions. Here, we test a link between whole-brain theories of semantics and the role of the left inferior frontal gyrus (LIFG) in controlled language performance using network control theory (NCT), a branch of systems engineering. Specifically, we examined whether two properties of node controllability, boundary and modal controllability, were linked to semantic selection and retrieval on sentence completion and verb generation tasks. We tested whether the controllability of the left IFG moderated language selection and retrieval costs and the effects of continuous θ burst stimulation (cTBS), an inhibitory form of transcranial magnetic stimulation (TMS) on behavior in 41 human subjects (25 active, 16 sham). We predicted that boundary controllability, a measure of the theoretical ability of a node to integrate and segregate brain networks, would be linked to word selection in the contextually-rich sentence completion task. In contrast, we expected that modal controllability, a measure of the theoretical ability of a node to drive the brain into specifically hard-to-reach states, would be linked to retrieval on the low-context verb generation task. Boundary controllability was linked to selection and to the ability of TMS to reduce response latencies on the sentence completion task. In contrast, modal controllability was not linked to performance on the tasks or TMS effects. Overall, our results suggest a link between the network integrating role of the LIFG and selection and the overall semantic demands of sentence completion.
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19
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Chiêm B, Crevecoeur F, Delvenne JC. Structure-informed functional connectivity driven by identifiable and state-specific control regions. Netw Neurosci 2021; 5:591-613. [PMID: 34189379 PMCID: PMC8233121 DOI: 10.1162/netn_a_00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states. Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Jean-Charles Delvenne
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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20
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Cai W, Ryali S, Pasumarthy R, Talasila V, Menon V. Dynamic causal brain circuits during working memory and their functional controllability. Nat Commun 2021; 12:3314. [PMID: 34188024 PMCID: PMC8241851 DOI: 10.1038/s41467-021-23509-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 04/30/2021] [Indexed: 02/04/2023] Open
Abstract
Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.
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Affiliation(s)
- Weidong Cai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Srikanth Ryali
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramkrishna Pasumarthy
- Department of Electrical Engineering, Robert Bosch Center of Data Sciences and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India
| | - Viswanath Talasila
- Department of Electronics and Telecommunication Engineering, Center for Imaging Technologies, M.S. Ramaiah Institute of Technology, Bengaluru, India
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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21
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Kringelbach ML, Deco G. Brain States and Transitions: Insights from Computational Neuroscience. Cell Rep 2021; 32:108128. [PMID: 32905760 DOI: 10.1016/j.celrep.2020.108128] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/25/2022] Open
Abstract
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia.
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22
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Momi D, Ozdemir RA, Tadayon E, Boucher P, Shafi MM, Pascual-Leone A, Santarnecchi E. Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation. Neuroimage 2021; 229:117698. [PMID: 33385561 PMCID: PMC9094638 DOI: 10.1016/j.neuroimage.2020.117698] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/09/2020] [Accepted: 12/18/2020] [Indexed: 12/25/2022] Open
Abstract
Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.
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Affiliation(s)
- Davide Momi
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States; Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Italy
| | - Recep A Ozdemir
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Ehsan Tadayon
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Pierre Boucher
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Mouhsin M Shafi
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston MA; Department of Neurology, Harvard Medical School, Boston, MA, United States; Guttmann Brain Health Institut, Guttmann Institut, Universitat Autonoma, Barcelona, Spain
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, United States; Department of Neurology, Harvard Medical School, Boston, MA, United States.
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23
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Deco G, Kringelbach ML. Turbulent-like Dynamics in the Human Brain. Cell Rep 2020; 33:108471. [PMID: 33296654 PMCID: PMC7725672 DOI: 10.1016/j.celrep.2020.108471] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/07/2020] [Accepted: 11/11/2020] [Indexed: 12/11/2022] Open
Abstract
Turbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of the brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1,003 healthy participants, we demonstrate turbulent-like human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed turbulent-like dynamics, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the exponential distance rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulent-like brain activity and optimal brain function. Overall, our results reveal a way of analyzing and modeling whole-brain dynamics that suggests a turbulent-like dynamic intrinsic backbone facilitating large-scale network communication.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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Singh MF, Braver TS, Cole MW, Ching S. Estimation and validation of individualized dynamic brain models with resting state fMRI. Neuroimage 2020; 221:117046. [PMID: 32603858 PMCID: PMC7875185 DOI: 10.1016/j.neuroimage.2020.117046] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/08/2020] [Accepted: 06/08/2020] [Indexed: 11/25/2022] Open
Abstract
A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.
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Affiliation(s)
- Matthew F Singh
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA; Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| | - Todd S Braver
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
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25
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Kyriakis P, Pequito S, Bogdan P. On the effects of memory and topology on the controllability of complex dynamical networks. Sci Rep 2020; 10:17346. [PMID: 33060617 PMCID: PMC7562949 DOI: 10.1038/s41598-020-74269-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 09/29/2020] [Indexed: 11/09/2022] Open
Abstract
Recent advances in network science, control theory, and fractional calculus provide us with mathematical tools necessary for modeling and controlling complex dynamical networks (CDNs) that exhibit long-term memory. Selecting the minimum number of driven nodes such that the network is steered to a prescribed state is a key problem to guarantee that complex networks have a desirable behavior. Therefore, in this paper, we study the effects of long-term memory and of the topological properties on the minimum number of driven nodes and the required control energy. To this end, we introduce Gramian-based methods for optimal driven node selection for complex dynamical networks with long-term memory and by leveraging the structure of the cost function, we design a greedy algorithm to obtain near-optimal approximations in a computationally efficiently manner. We investigate how the memory and topological properties influence the control effort by considering Erdős-Rényi, Barabási-Albert and Watts-Strogatz networks whose temporal dynamics follow a fractional order state equation. We provide evidence that scale-free and small-world networks are easier to control in terms of both the number of required actuators and the average control energy. Additionally, we show how our method could be applied to control complex networks originating from the human brain and we discover that certain brain cortex regions have a stronger impact on the controllability of network than others.
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Affiliation(s)
- Panagiotis Kyriakis
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA.
| | - Sérgio Pequito
- Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA
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26
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Ebrahimi A, Nowzari-Dalini A, Jalili M, Masoudi-Nejad A. Target controllability with minimal mediators in complex biological networks. Genomics 2020; 112:4938-4944. [PMID: 32905831 DOI: 10.1016/j.ygeno.2020.09.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/27/2020] [Accepted: 09/03/2020] [Indexed: 01/02/2023]
Abstract
Controllability of a complex network system is related to finding a set of minimum number of nodes, known as drivers, controlling which allows having a full control on the dynamics of the network. For some applications, only a portion of the network is required to be controlled, for which target control has been proposed. Often, along the controlling route from driver nodes to target nodes, some mediators (intermediate nodes) are also unwillingly controlled, which might cause various side effects. In controlling cancerous cells, unwillingly controlling healthy cells, might result in weakening them, thus affecting the immune system against cancer. This manuscript proposes a suitable candidate solution to the problem of finding minimum number of driver nodes under minimal mediators. Although many others have attempted to develop algorithms to find minimum number of drivers for target control, the newly proposed algorithm is the first one that is capable of achieving this goal and at the same time, keeping the number of the mediators to a minimum. The proposed controllability condition, based on path lengths between node pairs, meets Kalman's controllability rank condition and can be applied on directed networks. Our results show that the path length is a major determinant of in properties of the target control under minimal mediators. As the average path length becomes larger, the ratio of drivers to target nodes decreases and the ratio of mediators to targets increases. The proposed methodology has potential applications in biological networks. The source code of the algorithm and the networks that have been used are available from the following link: https://github.com/LBBSoft/Target-Control-with-Minimal-Mediators.git.
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Affiliation(s)
- Ali Ebrahimi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Mahdi Jalili
- School of Engineering, RMIT University, Melbourne, Australia.
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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27
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Stiso J, Corsi MC, Vettel JM, Garcia J, Pasqualetti F, De Vico Fallani F, Lucas TH, Bassett DS. Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior. J Neural Eng 2020; 17:046018. [PMID: 32369802 PMCID: PMC7734596 DOI: 10.1088/1741-2552/ab9064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. APPROACH Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. MAIN RESULTS We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention. SIGNIFICANCE The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.
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Affiliation(s)
- Jennifer Stiso
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marie-Constance Corsi
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Jean M. Vettel
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Javier Garcia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Timothy H. Lucas
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
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Structural Controllability Predicts Functional Patterns and Brain Stimulation Benefits Associated with Working Memory. J Neurosci 2020; 40:6770-6778. [PMID: 32690618 DOI: 10.1523/jneurosci.0531-20.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/05/2020] [Accepted: 07/08/2020] [Indexed: 01/08/2023] Open
Abstract
The brain is an inherently dynamic system, and much work has focused on the ability to modify neural activity through both local perturbations and changes in the function of global network ensembles. Network controllability is a recent concept in network neuroscience that purports to predict the influence of individual cortical sites on global network states and state changes, thereby creating a unifying account of local influences on global brain dynamics. While this notion is accepted in engineering science, it is subject to ongoing debates in neuroscience as empirical evidence linking network controllability to brain activity and human behavior remains scarce. Here, we present an integrated set of multimodal brain-behavior relationships derived from fMRI, diffusion tensor imaging, and online repetitive transcranial magnetic stimulation (rTMS) applied during an individually calibrated working memory task performed by individuals of both sexes. The modes describing the structural network system dynamics showed direct relationships to brain activity associated with task difficulty, with difficult-to-reach modes contributing to functional brain states in the hard task condition. Modal controllability (a measure quantifying the contribution of difficult-to-reach modes) at the stimulated site predicted both fMRI activations associated with increasing task difficulty and rTMS benefits on task performance. Furthermore, fMRI explained 64% of the variance between modal controllability and the working memory benefit associated with 5 Hz online rTMS. These results therefore provide evidence toward the functional validity of network control theory, and outline a clear technique for integrating structural network topology and functional activity to predict the influence of stimulation on subsequent behavior.SIGNIFICANCE STATEMENT The network controllability concept proposes that specific cortical nodes are able to steer the brain into certain physiological states. By applying external perturbation to these control nodes, it is theorized that brain stimulation is able to selectively target difficult-to-reach states, potentially aiding processing and improving performance on cognitive tasks. The current study used rTMS and fMRI during a working memory task to test this hypothesis. We demonstrate that network controllability correlates with fMRI modulation because of working memory load and with the behavioral improvements that result from a multivisit intervention using 5 Hz rTMS. This study demonstrates the validity of network controllability and offers a new targeting approach to improve efficacy.
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29
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Tang E, Ju H, Baum GL, Roalf DR, Satterthwaite TD, Pasqualetti F, Bassett DS. Control of brain network dynamics across diverse scales of space and time. Phys Rev E 2020; 101:062301. [PMID: 32688528 PMCID: PMC8728948 DOI: 10.1103/physreve.101.062301] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 03/12/2020] [Indexed: 12/30/2022]
Abstract
The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with activity on short length scales and slow timescales. Conversely, brain regions that facilitate control of difficult transitions are associated with activity on long length scales and fast timescales. Built on linear dynamical models, our results offer parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.
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Affiliation(s)
- Evelyn Tang
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Max Planck Institute for Dynamics and Self-Organization, Göttingen 37079, Germany
| | - Harang Ju
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Neuroscience Graduate Program, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Graham L Baum
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Neuroscience Graduate Program, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, California 92521, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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30
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Prando G, Zorzi M, Bertoldo A, Corbetta M, Zorzi M, Chiuso A. Sparse DCM for whole-brain effective connectivity from resting-state fMRI data. Neuroimage 2020; 208:116367. [DOI: 10.1016/j.neuroimage.2019.116367] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/12/2022] Open
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Bahadorian M, Alimohammadi H, Mozaffari T, Tabar MRR, Peinke J, Lehnertz K. A topology-dynamics-based control strategy for multi-dimensional complex networked dynamical systems. Sci Rep 2019; 9:19831. [PMID: 31882634 PMCID: PMC6934796 DOI: 10.1038/s41598-019-56259-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 12/06/2019] [Indexed: 11/30/2022] Open
Abstract
Complex systems are omnipresent and play a vital role in in our every-day lives. Adverse behavior of such systems has generated considerable interest in being able to control complex systems modeled as networks. Here, we propose a topology-dynamics-based approach for controlling complex systems modeled as networks of coupled multi-dimensional dynamical entities. For given dynamics and topology, we introduce an efficient scheme to identify in polynomial time a finite set of driver nodes, which – when endowed with the control function – steer the network to the desired behavior. We demonstrate the high suitability of our approach by controlling various networked multi-dimensional dynamics, coupled onto different topologies.
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32
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Conti F, Van Gorder RA. The role of network structure and time delay in a metapopulation Wilson--Cowan model. J Theor Biol 2019; 477:1-13. [PMID: 31181240 DOI: 10.1016/j.jtbi.2019.05.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 04/23/2019] [Accepted: 05/16/2019] [Indexed: 01/11/2023]
Abstract
We study the dynamics of a network Wilson--Cowan model (a system of connected Wilson--Cowan oscillators) for interacting excitatory and inhibitory neuron populations with time delays. Each node in this model corresponds to a population of neurons, including excitatory and inhibitory subpopulations, and hence it can be viewed as a metapopulation model. It is known that information transfer within each cortical area is not instantaneous, and therefore we consider a system of delay differential equations with two different kinds of discrete delays. We account for the time delay in information propagation between individual excitatory and inhibitory subpopulations at each node via intra-node time delays, and we account for time delay in information propagation between neuron populations at different nodes with inter-node time delays. The biologically relevant resting state solutions are oscillatory (stable limit cycles). After determining the influence of the coupling parameters between nodes, the intra-node delays, and the inter-node delays on the dynamics of the two coupled Wilson--Cowan oscillators, we then explore a variety of larger networks of 16 and 100 nodes, in order to determine how the network topology will influence time delayed Wilson--Cowan dynamics. We find that network structure can regularize or deregularize the dynamics, with networks of higher mean degree permitting stable limit cycles and networks with smaller mean degree yielding less regular dynamics (which may range from chaotic solutions, to solutions for which limit cycles collapse into steady states, which are biologically undesirable compared with the preferred stable limit cycles). Furthermore, heterogeneity in the degree distribution of the network (resulting from networks with nodes of varying degree) can result in asynchronous dynamics, even if at each node the local dynamics are that of a limit cycle, in contrast to the synchronization of dynamics between nodes seen when the degree of all nodes is equal. This suggests that homogeneous and well-connected networks permit robust limit cycles under time-delayed Wilson--Cowan dynamics, whereas heterogeneous or poorly connected networks may fail to provide such desirable dynamics, a phenomena akin to structural loss of neuron connections in neurodegenerative diseases.
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Affiliation(s)
- Federica Conti
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom; Institut de Neurosciences de la Timone, Aix-Marseille Université, CNRS, Faculté de Médecine, 27 boulevard Jean Moulin, Marseille 13005, France
| | - Robert A Van Gorder
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom; Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand.
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33
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Suweis S, Tu C, Rocha RP, Zampieri S, Zorzi M, Corbetta M. Brain controllability: Not a slam dunk yet. Neuroimage 2019; 200:552-555. [PMID: 31291605 DOI: 10.1016/j.neuroimage.2019.07.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 06/27/2019] [Accepted: 07/04/2019] [Indexed: 10/26/2022] Open
Abstract
In our recent article [1] published in this journal we provide quantitative evidence to show that there are warnings and caveats in the way Gu and collaborators [2] define controllability of brain networks and measure the contribution of each of its nodes. The comment by Pasqualetti et al. [3] confirms the need to go beyond the methodology and approach presented in Gu et al.'s original work. In fact, they recognize that "the source of confusion is due to the fact that assessing controllability via numerical analysis typically leads to ill-conditioned problems, and thus often generates results that are difficult to interpret". This is indeed the first warning we discussed in [1]: our work was not meant to prove that brain networks are not controllable from one node, rather we wished to highlight that the one node controllability framework and all consequent results were not properly justified based on the methodology presented in Gu et al. [2]. We used in our work the same method of Gu et al. not because we believe it is the best methodology, but because we extensively investigated it with the aim of replicating, testing, and extending their results. The warning and caveats we have proposed are the results of this investigation. Indeed, on the basis of our controllability analyses of multiple human brain networks datasets, we concluded: "The λmin(WK) are statistically compatible with zero and thus the associated controllability Gramian cannot be inverted1. These results show that it is not possible to infer one node controllability of the brain numerically". Hence both groups agree that one node controllability cannot be inferred numerically.
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Affiliation(s)
- Samir Suweis
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy.
| | - Chengyi Tu
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, USA; School of Ecology and Environmental Science, Yunnan University, Yunnan, China
| | - Rodrigo P Rocha
- Padova Neuroscience Center, Università di Padova, Padova, Italy; Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil; Departamento de Física, Universidade Federal de Santa Catarina, 88040-900, Florianópolis, SC, Brazil
| | - Sandro Zampieri
- Padova Neuroscience Center, Università di Padova, Padova, Italy; Dipartimento di Ingegneria dell'informazione, Università di Padova, Padova, Italy
| | - Marzo Zorzi
- Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy; Fondazione Ospedale San Camillo IRCCS, Venezia, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, Università di Padova, Padova, Italy; Dipartimento di Neuroscienze, Universita' di Padova, Padova, Italy; Departments of Neurology, Radiology, Neuroscience, and Bioengineering, Washington University, School of Medicine, St. Louis, USA
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34
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Pasqualetti F, Gu S, Bassett DS. RE: Warnings and caveats in brain controllability. Neuroimage 2019; 197:586-588. [PMID: 31075390 DOI: 10.1016/j.neuroimage.2019.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 04/10/2019] [Accepted: 05/01/2019] [Indexed: 01/06/2023] Open
Abstract
The use of network control theory to analyze the organization of white matter fibers in the human brain has the potential to enable mechanistic theories of cognition, and to inform the development of novel diagnostics and treatments for neurological disease and psychiatric disorders (Gu et al., 2015). The recent article (Tu et al., 2018) aims to challenge several of the contributions of (Gu et al., 2015), and particularly the conclusions that brain networks are theoretically controllable from single regions, and that brain networks feature no specific controllability profiles when compared to random network models. Here we provide additional theoretical arguments in support of (Gu et al., 2015) and against the results and methodologies used in (Tu et al., 2018), thus settling that (i) brain networks are controllable from a single region, (ii) brain networks require large control energy, and (iii) brain networks feature distinctive controllability properties with respect to a class of random network models.
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Affiliation(s)
- Fabio Pasqualetti
- Department of Mechanical Engineering, University of California at Riverside, USA.
| | - Shi Gu
- University of Electronic Science and Technology of China, China.
| | - Danielle S Bassett
- Departments of Bioengineering, Physics & Astronomy, Neurology, Psychiatry, Electrical & Systems Engineering, University of Pennsylvania, USA.
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35
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Papo D. Neurofeedback: Principles, appraisal, and outstanding issues. Eur J Neurosci 2019; 49:1454-1469. [PMID: 30570194 DOI: 10.1111/ejn.14312] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/16/2022]
Abstract
Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.
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Affiliation(s)
- David Papo
- SCALab, CNRS, Université de Lille, Villeneuve d'Ascq, France
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36
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Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 2019; 22:289-296. [PMID: 30664771 DOI: 10.1038/s41593-018-0312-0] [Citation(s) in RCA: 237] [Impact Index Per Article: 47.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/26/2018] [Indexed: 12/21/2022]
Abstract
The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.
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37
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Medaglia JD. Clarifying cognitive control and the controllable connectome. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2019; 10:e1471. [PMID: 29971940 PMCID: PMC6642819 DOI: 10.1002/wcs.1471] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 05/28/2018] [Accepted: 05/29/2018] [Indexed: 01/08/2023]
Abstract
Cognitive control researchers aim to describe the processes that support adaptive cognition to achieve specific goals. Control theorists consider how to influence the state of systems to reach certain user-defined goals. In brain networks, some conceptual and lexical similarities between cognitive control and control theory offer appealing avenues for scientific discovery. However, these opportunities also come with the risk of conceptual confusion. Here, I suggest that each field of inquiry continues to produce novel and distinct insights. Then, I describe opportunities for synergistic research at the intersection of these subdisciplines with a critical stance that reduces the risk of conceptual confusion. Through this exercise, we can observe that both cognitive neuroscience and systems engineering have much to contribute to cognitive control research in human brain networks. This article is categorized under: Neuroscience > Cognition Computer Science > Neural Networks Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Abstract
Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.
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Kenett YN, Medaglia JD, Beaty RE, Chen Q, Betzel RF, Thompson-Schill SL, Qiu J. Driving the brain towards creativity and intelligence: A network control theory analysis. Neuropsychologia 2018; 118:79-90. [PMID: 29307585 PMCID: PMC6034981 DOI: 10.1016/j.neuropsychologia.2018.01.001] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 11/20/2017] [Accepted: 01/02/2018] [Indexed: 01/21/2023]
Abstract
High-level cognitive constructs, such as creativity and intelligence, entail complex and multiple processes, including cognitive control processes. Recent neurocognitive research on these constructs highlight the importance of dynamic interaction across neural network systems and the role of cognitive control processes in guiding such a dynamic interaction. How can we quantitatively examine the extent and ways in which cognitive control contributes to creativity and intelligence? To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in distinct measures of creative ability and intelligence. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that intelligence is related to the ability to "drive" the brain system into easy to reach neural states by the right inferior parietal lobe and lower integration abilities in the left retrosplenial cortex. We also find that creativity is related to the ability to "drive" the brain system into difficult to reach states by the right dorsolateral prefrontal cortex (inferior frontal junction) and higher integration abilities in sensorimotor areas. Furthermore, we found that different facets of creativity-fluency, flexibility, and originality-relate to generally similar but not identical network controllability processes. We relate our findings to general theories on intelligence and creativity.
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Affiliation(s)
- Yoed N. Kenett
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John D. Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA 19104, USA
| | - Roger E. Beaty
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA USA
| | - Qunlin Chen
- School of Psychology, Southwest University, Chongqing 400715, China
| | - Richard F. Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing 400715, China
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