External drivers of BOLD signal’s non-stationarity

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce erro...

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Published inPloS one Vol. 17; no. 9; p. e0257580
Main Authors Ashourvan, Arian, Pequito, Sérgio, Bertolero, Maxwell, Kim, Jason Z., Bassett, Danielle S., Litt, Brian
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 19.09.2022
Public Library of Science (PLoS)
Subjects
Biology and Life Sciences
Brain
Brain - diagnostic imaging
Brain - physiology
Brain mapping
Brain research
Computational neuroscience
Computer and Information Sciences
Connectome
Consortia
Data acquisition
Dynamical systems
Engineering and Technology
External stimuli
Functional magnetic resonance imaging
Hemodynamics
Humans
Image acquisition
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical models
Medicine and Health Sciences
Nervous system
Neural circuitry
Neuroimaging
Neurosciences
Parameters
Physical Sciences
Research and Analysis Methods
Social Sciences
Stimulation
System theory
United States
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0257580

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Abstract A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal’s external drivers and shines a light on the likely external sources contributing to the BOLD signal’s non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain’s time-varying functional dynamics.
AbstractList A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.
A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.
Audience Academic
Author Kim, Jason Z.
Ashourvan, Arian
Bassett, Danielle S.
Bertolero, Maxwell
Litt, Brian
Pequito, Sérgio
AuthorAffiliation 4 Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
8 Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States of America
1 Department of Psychology, University of Kansas, Lawrence, KS, United States of America
3 Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
7 Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America
University of Cambridge, UNITED KINGDOM
5 Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States of America
2 Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
6 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
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– name: 4 Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
– name: 1 Department of Psychology, University of Kansas, Lawrence, KS, United States of America
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Snippet A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions...
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SubjectTerms Biology and Life Sciences
Brain
Brain - diagnostic imaging
Brain - physiology
Brain mapping
Brain research
Computational neuroscience
Computer and Information Sciences
Connectome
Consortia
Data acquisition
Dynamical systems
Engineering and Technology
External stimuli
Functional magnetic resonance imaging
Hemodynamics
Humans
Image acquisition
Magnetic resonance
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical models
Medicine and Health Sciences
Nervous system
Neural circuitry
Neuroimaging
Neurosciences
Parameters
Physical Sciences
Research and Analysis Methods
Social Sciences
Stimulation
System theory
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Title External drivers of BOLD signal’s non-stationarity
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http://dx.doi.org/10.1371/journal.pone.0257580
Volume 17
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