1
|
El Zghir RK, Gabay NC, Robinson PA. Unified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity. Front Comput Neurosci 2024; 18:1335130. [PMID: 39286332 PMCID: PMC11403587 DOI: 10.3389/fncom.2024.1335130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 08/07/2024] [Indexed: 09/19/2024] Open
Abstract
A compact description of the frequency structure and topography of human alpha-band rhythms is obtained by use of the first four brain activity eigenmodes previously derived from corticothalamic neural field theory. Just two eigenmodes that overlap in frequency are found to reproduce the observed topography of the classical alpha rhythm for subjects with a single, occipitally concentrated alpha peak in their electroencephalograms. Alpha frequency splitting and relative amplitudes of double alpha peaks are explored analytically and numerically within this four-mode framework using eigenfunction expansion and perturbation methods. These effects are found to result primarily from the different eigenvalues and corticothalamic gains corresponding to the eigenmodes. Three modes with two non-overlapping frequencies suffice to reproduce the observed topography for subjects with a double alpha peak, where the appearance of a distinct second alpha peak requires an increase of the corticothalamic gain of higher eigenmodes relative to the first. Conversely, alpha blocking is inferred to be linked to a relatively small attention-dependent reduction of the gain of the relevant eigenmodes, whose effect is enhanced by the near-critical state of the brain and whose sign is consistent with inferences from neural field theory. The topographies and blocking of the mu and tau rhythms within the alpha-band are explained analogously via eigenmodes. Moreover, the observation of three rhythms in the alpha band is due to there being exactly three members of the first family of spatially nonuniform modes. These results thus provide a simple, unified description of alpha band rhythms and enable experimental observations of spectral structure and topography to be linked directly to theory and underlying physiology.
Collapse
Affiliation(s)
- Rawan Khalil El Zghir
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
- Northern Sydney Cancer Center, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - P A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| |
Collapse
|
2
|
Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [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: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
Collapse
Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| |
Collapse
|
3
|
Cao T, Pang JC, Segal A, Chen Y, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. Hum Brain Mapp 2024; 45:e26640. [PMID: 38445545 PMCID: PMC10915742 DOI: 10.1002/hbm.26640] [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: 05/30/2023] [Revised: 02/06/2024] [Accepted: 02/18/2024] [Indexed: 03/07/2024] Open
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes-eigenmodes-of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
Collapse
Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - James C. Pang
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Yu‐Chi Chen
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Kevin M. Aquino
- School of PhysicsUniversity of SydneyCamperdownNew South WalesAustralia
| | - Michael Breakspear
- School of Psychological SciencesUniversity of NewcastleCallaghanNew South WalesAustralia
| | - Alex Fornito
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| |
Collapse
|
4
|
Pang JC, Aquino KM, Oldehinkel M, Robinson PA, Fulcher BD, Breakspear M, Fornito A. Geometric constraints on human brain function. Nature 2023; 618:566-574. [PMID: 37258669 PMCID: PMC10266981 DOI: 10.1038/s41586-023-06098-1] [Citation(s) in RCA: 71] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/18/2023] [Indexed: 06/02/2023]
Abstract
The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. The classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected by a complex array of axonal fibres1-3. However, predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity4-6, suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity7,8. Here, we confirm these theoretical predictions by analysing human magnetic resonance imaging data acquired under spontaneous and diverse task-evoked conditions. Specifically, we show that cortical and subcortical activity can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brain's geometry (that is, its shape) rather than from modes of complex interregional connectivity, as classically assumed. We then use these geometric modes to show that task-evoked activations across over 10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning over 60 mm. Finally, we confirm predictions that the close link between geometry and function is explained by a dominant role for wave-like activity, showing that wave dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views and identify a previously underappreciated role of geometry in shaping function, as predicted by a unifying and physically principled model of brain-wide dynamics.
Collapse
Affiliation(s)
- James C Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
| | - Kevin M Aquino
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
- BrainKey Inc., San Francisco, CA, USA
| | - Marianne Oldehinkel
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Peter A Robinson
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
| | - Ben D Fulcher
- School of Physics, University of Sydney, Camperdown, New South Wales, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Callaghan, New South Wales, Australia
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| |
Collapse
|
5
|
Cao T, Pang JC, Segal A, Chen YC, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.529328. [PMID: 36909539 PMCID: PMC10002616 DOI: 10.1101/2023.02.26.529328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
Collapse
Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - James C Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Yu-Chi Chen
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Physics Rd, Camperdown NSW 2006, Australia
| | - Michael Breakspear
- School of Psychological Sciences, University of Newcastle, University Dr, Callaghan NSW 2308, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| |
Collapse
|
6
|
Clark KB. Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks. BIOLOGY 2023; 12:352. [PMID: 36979044 PMCID: PMC10045557 DOI: 10.3390/biology12030352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa's arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.
Collapse
Affiliation(s)
- Kevin B. Clark
- Cures Within Reach, Chicago, IL 60602, USA;
- Felidae Conservation Fund, Mill Valley, CA 94941, USA
- Campus and Domain Champions Program, Multi-Tier Assistance, Training, and Computational Help (MATCH) Track, National Science Foundation’s Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS), https://access-ci.org/
- Expert Network, Penn Center for Innovation, University of Pennsylvania, Philadelphia, PA 19104, USA
- Network for Life Detection (NfoLD), NASA Astrobiology Program, NASA Ames Research Center, Mountain View, CA 94035, USA
- Multi-Omics and Systems Biology & Artificial Intelligence and Machine Learning Analysis Working Groups, NASA GeneLab, NASA Ames Research Center, Mountain View, CA 94035, USA
- Frontier Development Lab, NASA Ames Research Center, Mountain View, CA 94035, USA & SETI Institute, Mountain View, CA 94043, USA
- Peace Innovation Institute, The Hague 2511, Netherlands & Stanford University, Palo Alto, CA 94305, USA
- Shared Interest Group for Natural and Artificial Intelligence (sigNAI), Max Planck Alumni Association, 14057 Berlin, Germany
- Biometrics and Nanotechnology Councils, Institute for Electrical and Electronics Engineers (IEEE), New York, NY 10016, USA
| |
Collapse
|
7
|
Cabral J, Fernandes FF, Shemesh N. Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI. Nat Commun 2023; 14:375. [PMID: 36746938 PMCID: PMC9902553 DOI: 10.1038/s41467-023-36025-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/12/2023] [Indexed: 02/08/2023] Open
Abstract
Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the addition of isoflurane. Peaking in power within clear anatomical boundaries, these oscillatory modes point to an emergent systemic property. This work provides additional insight into the origin of oscillations detected in fMRI and the organizing principles underpinning spontaneous long-range functional connectivity.
Collapse
Affiliation(s)
- Joana Cabral
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal.
- ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal.
| | - Francisca F Fernandes
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Noam Shemesh
- Preclinical MRI Lab, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| |
Collapse
|
8
|
John YJ, Sawyer KS, Srinivasan K, Müller EJ, Munn BR, Shine JM. It's about time: Linking dynamical systems with human neuroimaging to understand the brain. Netw Neurosci 2022; 6:960-979. [PMID: 36875012 PMCID: PMC9976648 DOI: 10.1162/netn_a_00230] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022] Open
Abstract
Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.
Collapse
Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA
| | - Kayle S. Sawyer
- Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Boston VA Healthcare System, Boston, MA, USA
- Sawyer Scientific, LLC, Boston, MA, USA
| | - Karthik Srinivasan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eli J. Müller
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - Brandon R. Munn
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
| |
Collapse
|
9
|
Pathak A, Roy D, Banerjee A. Whole-Brain Network Models: From Physics to Bedside. Front Comput Neurosci 2022; 16:866517. [PMID: 35694610 PMCID: PMC9180729 DOI: 10.3389/fncom.2022.866517] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.
Collapse
Affiliation(s)
| | - Dipanjan Roy
- Centre for Brain Science and Applications, School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, India
| | - Arpan Banerjee
- National Brain Research Centre, Gurgaon, India
- *Correspondence: Arpan Banerjee
| |
Collapse
|