Network communication models improve the behavioral and functional predictive utility of the human structural connectome
The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-functio...
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| Vydané v: | Network neuroscience (Cambridge, Mass.) Ročník 4; číslo 4; s. 980 - 1006 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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One Rogers Street, Cambridge, MA 02142-1209, USA
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01.11.2020
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| ISSN: | 2472-1751, 2472-1751 |
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| Abstract | The connectome provides the structural substrate facilitating communication
between brain regions. We aimed to establish whether accounting for polysynaptic
communication in structural connectomes would improve prediction of
interindividual variation in behavior as well as increase structure-function
coupling strength. Connectomes were mapped for 889 healthy adults participating
in the Human Connectome Project. To account for polysynaptic signaling,
connectomes were transformed into communication matrices for each of 15
different network communication models. Communication matrices were (a) used to
perform predictions of five data-driven behavioral dimensions and (b) correlated
to resting-state functional connectivity (FC). While FC was the most accurate
predictor of behavior, communication models, in particular communicability and
navigation, improved the performance of structural connectomes. Communication
also strengthened structure-function coupling, with the navigation and shortest
paths models leading to 35–65% increases in association strength
with FC. We combined behavioral and functional results into a single ranking
that provides insight into which communication models may more faithfully
recapitulate underlying neural signaling patterns. Comparing results across
multiple connectome mapping pipelines suggested that modeling polysynaptic
communication is particularly beneficial in sparse high-resolution connectomes.
We conclude that network communication models can augment the functional and
behavioral predictive utility of the human structural connectome.
Brain network communication models aim to describe the patterns of large-scale
neural signaling that facilitate functional interactions between brain regions.
While information can be directly communicated between anatomically connected
regions, signaling between disconnected areas must occur via a sequence of
intermediate regions. We investigated a number of candidate models of connectome
communication and found that they improved structure-function coupling and the
extent to which structural connectomes can predict interindividual variation in
behavior. Comparing the behavioral and functional predictive utility of
different models provided initial insight into which conceptualizations of
network communication may more faithfully recapitulate biological neural
signaling. Our results suggest network communication models as a promising
avenue to unite our understanding of brain structure, brain function, and human
behavior. |
|---|---|
| AbstractList | The connectome provides the structural substrate facilitating communication
between brain regions. We aimed to establish whether accounting for polysynaptic
communication in structural connectomes would improve prediction of
interindividual variation in behavior as well as increase structure-function
coupling strength. Connectomes were mapped for 889 healthy adults participating
in the Human Connectome Project. To account for polysynaptic signaling,
connectomes were transformed into communication matrices for each of 15
different network communication models. Communication matrices were (a) used to
perform predictions of five data-driven behavioral dimensions and (b) correlated
to resting-state functional connectivity (FC). While FC was the most accurate
predictor of behavior, communication models, in particular communicability and
navigation, improved the performance of structural connectomes. Communication
also strengthened structure-function coupling, with the navigation and shortest
paths models leading to 35–65% increases in association strength
with FC. We combined behavioral and functional results into a single ranking
that provides insight into which communication models may more faithfully
recapitulate underlying neural signaling patterns. Comparing results across
multiple connectome mapping pipelines suggested that modeling polysynaptic
communication is particularly beneficial in sparse high-resolution connectomes.
We conclude that network communication models can augment the functional and
behavioral predictive utility of the human structural connectome.
Brain network communication models aim to describe the patterns of large-scale
neural signaling that facilitate functional interactions between brain regions.
While information can be directly communicated between anatomically connected
regions, signaling between disconnected areas must occur via a sequence of
intermediate regions. We investigated a number of candidate models of connectome
communication and found that they improved structure-function coupling and the
extent to which structural connectomes can predict interindividual variation in
behavior. Comparing the behavioral and functional predictive utility of
different models provided initial insight into which conceptualizations of
network communication may more faithfully recapitulate biological neural
signaling. Our results suggest network communication models as a promising
avenue to unite our understanding of brain structure, brain function, and human
behavior. The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.Author Summary: Brain network communication models aim to describe the patterns of large-scale neural signaling that facilitate functional interactions between brain regions. While information can be directly communicated between anatomically connected regions, signaling between disconnected areas must occur via a sequence of intermediate regions. We investigated a number of candidate models of connectome communication and found that they improved structure-function coupling and the extent to which structural connectomes can predict interindividual variation in behavior. Comparing the behavioral and functional predictive utility of different models provided initial insight into which conceptualizations of network communication may more faithfully recapitulate biological neural signaling. Our results suggest network communication models as a promising avenue to unite our understanding of brain structure, brain function, and human behavior. The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome. The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome. Brain network communication models aim to describe the patterns of large-scale neural signaling that facilitate functional interactions between brain regions. While information can be directly communicated between anatomically connected regions, signaling between disconnected areas must occur via a sequence of intermediate regions. We investigated a number of candidate models of connectome communication and found that they improved structure-function coupling and the extent to which structural connectomes can predict interindividual variation in behavior. Comparing the behavioral and functional predictive utility of different models provided initial insight into which conceptualizations of network communication may more faithfully recapitulate biological neural signaling. Our results suggest network communication models as a promising avenue to unite our understanding of brain structure, brain function, and human behavior. AbstractThe connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome. The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome. Author Summary Brain network communication models aim to describe the patterns of large-scale neural signaling that facilitate functional interactions between brain regions. While information can be directly communicated between anatomically connected regions, signaling between disconnected areas must occur via a sequence of intermediate regions. We investigated a number of candidate models of connectome communication and found that they improved structure-function coupling and the extent to which structural connectomes can predict interindividual variation in behavior. Comparing the behavioral and functional predictive utility of different models provided initial insight into which conceptualizations of network communication may more faithfully recapitulate biological neural signaling. Our results suggest network communication models as a promising avenue to unite our understanding of brain structure, brain function, and human behavior. |
| Author | Zalesky, Andrew Seguin, Caio Tian, Ye |
| Author_xml | – sequence: 1 givenname: Caio surname: Seguin fullname: Seguin, Caio email: caioseguin@gmail.com organization: Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia – sequence: 2 givenname: Ye surname: Tian fullname: Tian, Ye organization: Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia – sequence: 3 givenname: Andrew surname: Zalesky fullname: Zalesky, Andrew organization: Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia |
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| PublicationTitle | Network neuroscience (Cambridge, Mass.) |
| PublicationYear | 2020 |
| Publisher | MIT Press MIT Press Journals, The The MIT Press |
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between brain regions. We aimed to establish whether accounting for polysynaptic... The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic... AbstractThe connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for... |
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| SubjectTerms | Behavior Behavioral prediction Brain Brain network communication models Communication Connectomics Coupling Functional anatomy Human behavior Navigation Network neuroscience Neural networks Neural signaling Structure-function coupling Structure-function relationships Substrates |
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| Title | Network communication models improve the behavioral and functional predictive utility of the human structural connectome |
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