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Peel L, Peixoto TP, De Domenico M. Statistical inference links data and theory in network science. Nat Commun 2022; 13:6794. [PMID: 36357376 PMCID: PMC9649740 DOI: 10.1038/s41467-022-34267-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications. Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.
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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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Jamalabadi H, Zuberer A, Kumar VJ, Li M, Alizadeh S, Amani AM, Gaser C, Esterman M, Walter M. The missing role of gray matter in studying brain controllability. Netw Neurosci 2021; 5:198-210. [PMID: 33688612 PMCID: PMC7935040 DOI: 10.1162/netn_a_00174] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 10/23/2020] [Indexed: 12/16/2022] Open
Abstract
Brain controllability properties are normally derived from the white matter fiber tracts in which the neural substrate of the actual energy consumption, namely the gray matter, has been widely ignored. Here, we study the relationship between gray matter volume of regions across the whole cortex and their respective control properties derived from the structural architecture of the white matter fiber tracts. The data suggests that the ability of white fiber tracts to exhibit control at specific nodes not only depends on the connection strength of the structural connectome but additionally depends on gray matter volume at the host nodes. Our data indicate that connectivity strength and gray matter volume interact with respect to the brain’s control properties. Disentangling effects of the regional gray matter volume and connectivity strength, we found that frontal and sensory areas play crucial roles in controllability. Together these results suggest that structural and regional properties of the white matter and gray matter provide complementary information in studying the control properties of the intrinsic structural and functional architecture of the brain. Network control theory suggests that the functions of large-scale brain circuits can be partially described with respect to the ability of brain regions to steer brain activity to different states. This ability, often quantified in terms of controllability metrics, has normally been derived from the structural architecture of the white matter fiber tracts. However, gray matter as the substrate that engenders much of the neural processes is widely ignored in this context. In the present work, we study the relationship between regional gray matter volume and control properties across the whole cortex and provide evidence that control properties not only depend on the connection strength of the structural connectome but also depend on sufficient gray matter volume at the host nodes.
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Affiliation(s)
- Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Agnieszka Zuberer
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | | | - Meng Li
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Sarah Alizadeh
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germanys
| | - Ali Moradi Amani
- School of Engineering, RMIT University, Melbourne, Victoria, Australia
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Michael Esterman
- Boston University School of Medicine, Department of Psychiatry, Boston, MA, USA
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
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Resting fMRI-guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRI. Exp Brain Res 2021; 239:1165-1178. [PMID: 33560448 DOI: 10.1007/s00221-021-06036-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 01/08/2021] [Indexed: 12/31/2022]
Abstract
Traditional non-invasive imaging methods describe statistical associations of functional co-activation over time. They cannot easily establish hierarchies in communication as done in non-human animals using invasive methods. Here, we interleaved functional MRI (fMRI) recordings with non-invasive transcranial magnetic stimulation (TMS) to map causal communication between the frontal cortex and subcortical target structures including the subgenual anterior cingulate cortex (sgACC) and the amygdala. Seed-based correlation maps from each participant's resting fMRI scan determined individual stimulation sites with high temporal correlation to targets for the subsequent TMS/fMRI session(s). The resulting TMS/fMRI images were transformed to quantile responses, so that regions of high-/low-quantile response corresponded to the areas of the brain with the most positive/negative evoked response relative to the global brain response. We then modeled the average quantile response for a given region (e.g., structure or network) to determine whether TMS was effective in the relative engagement of the downstream targets. Both the sgACC and amygdala were differentially influenced by TMS. Furthermore, we found that the sgACC distributed brain network was modulated in response to fMRI-guided TMS. The amygdala, but not its distributed network, also responded to TMS. Our findings suggest that individual targeting and brain response measurements reflect causal circuit mapping to the sgACC and amygdala in humans. These results set the stage to further map circuits in the brain and link circuit pathway integrity to clinical intervention outcomes, especially when the intervention targets specific pathways and networks as is possible with TMS.
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Moral Framing and Mechanisms Influence Public Willingness to Optimize Cognition. JOURNAL OF COGNITIVE ENHANCEMENT 2020. [DOI: 10.1007/s41465-020-00190-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Medaglia JD, Kuersten A, Hamilton RH. Protecting Decision-Making in the Era of Neuromodulation. JOURNAL OF COGNITIVE ENHANCEMENT 2020. [DOI: 10.1007/s41465-020-00171-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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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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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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Tu C, Rocha RP, Corbetta M, Zampieri S, Zorzi M, Suweis S. Warnings and caveats in brain controllability. Neuroimage 2018; 176:83-91. [PMID: 29654874 PMCID: PMC6607911 DOI: 10.1016/j.neuroimage.2018.04.010] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 03/09/2018] [Accepted: 04/06/2018] [Indexed: 12/19/2022] Open
Abstract
A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their "controllability", drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.
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Affiliation(s)
- Chengyi Tu
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Rodrigo P Rocha
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Maurizio Corbetta
- Dipartimento di Neuroscienze, Università di Padova, Padova, Italy; Departments of Neurology, Radiology, Neuroscience, and Bioengineering, Washington University, School of Medicine, St. Louis, USA; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Sandro Zampieri
- Dipartimento di Ingegneria dell'informazione, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy
| | - Marco Zorzi
- Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy; IRCCS San Camillo Hospital Foundation, Venice, Italy
| | - S Suweis
- Dipartimento di Fisica e Astronomia, 'G. Galilei' & INFN, Università di Padova, Padova, Italy; Padova Neuroscience Center, Università di Padova, Padova, Italy.
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Yaden DB, Eichstaedt JC, Medaglia JD. The Future of Technology in Positive Psychology: Methodological Advances in the Science of Well-Being. Front Psychol 2018; 9:962. [PMID: 29967586 PMCID: PMC6016018 DOI: 10.3389/fpsyg.2018.00962] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 05/24/2018] [Indexed: 01/07/2023] Open
Abstract
Advances in biotechnology and information technology are poised to transform well-being research. This article reviews the technologies that we predict will have the most impact on both measurement and intervention in the field of positive psychology over the next decade. These technologies include: psychopharmacology, non-invasive brain stimulation, virtual reality environments, and big-data methods for large-scale multivariate analysis. Some particularly relevant potential costs and benefits to individual and collective well-being are considered for each technology as well as ethical considerations. As these technologies may substantially enhance the capacity of psychologists to intervene on and measure well-being, now is the time to discuss the potential promise and pitfalls of these technologies.
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Affiliation(s)
- David B. Yaden
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - John D. Medaglia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Psychology, Drexel University, Philadelphia, PA, United States
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