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Nguyen N, Hou T, Amico E, Zheng J, Huang H, Kaplan AD, Petri G, Goñi J, Kaufmann R, Zhao Y, Duong-Tran D, Shen L. Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics. ARXIV 2024:arXiv:2407.05060v2. [PMID: 39108288 PMCID: PMC11302673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/23/2024]
Abstract
Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.
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Affiliation(s)
- Nghi Nguyen
- Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Tao Hou
- Department of Computer Science, University of Oregon, Eugene, Oregon, USA
| | - Enrico Amico
- Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Jingyi Zheng
- Department of Mathematics and Statistics, Auburn University, Alabama, USA
| | - Huajun Huang
- Department of Mathematics and Statistics, Auburn University, Alabama, USA
| | - Alan D Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California, USA
| | - Giovanni Petri
- NPLab, Network Science Institute, Northeastern University London, London, UK
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- School of Biomedical Engineering, Purdue University, W. Lafayette, Indiana, USA
| | - Ralph Kaufmann
- Department of Mathematics, Purdue University, W. Lafayette, Indiana, USA
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, Connecticut, USA
| | - Duy Duong-Tran
- Department of Mathematics, U.S. Naval Academy, Annapolis, Maryland, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Moghaddam M, Dzemidzic M, Guerrero D, Liu M, Alessi J, Plawecki MH, Harezlak J, Kareken D, Goñi J. Tangent space functional reconfigurations in individuals at risk for alcohol use disorder. ARXIV 2024:arXiv:2405.15905v1. [PMID: 38827458 PMCID: PMC11142326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform functional connectomes (FCs) and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning in/from a task would be influenced by family history of alcohol use disorder (FHA) and other AUD risk factors. Multilinear regression model results showed that engaging and disengaging functional reconfiguration were driven by different AUD risk factors. Functional reconfiguration when engaging in the SST was negatively associated with recent drinking. When disengaging from the SST, however, functional reconfiguration was negatively associated with FHA. In both models, several other factors contributed to the explanation of functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration, and that it is related to AUD risk.
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Affiliation(s)
- Mahdi Moghaddam
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Daniel Guerrero
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
| | - Mintao Liu
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
| | - Jonathan Alessi
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin H Plawecki
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN USA
| | - Jaroslaw Harezlak
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA
- Indiana Alcohol Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA
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Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
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Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
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