Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

•The relation of brain function with the underlying structural wiring is complex.•We propose new structure-informed graph signal processing (GSP) filtering of functional data.•GSP-derived features allow accurate task decoding and individual fingerprinting.•Functional connectivity from filtered data...

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Vydáno v:NeuroImage (Orlando, Fla.) Ročník 250; s. 118970
Hlavní autoři: Griffa, Alessandra, Amico, Enrico, Liégeois, Raphaël, Van De Ville, Dimitri, Preti, Maria Giulia
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Inc 15.04.2022
Elsevier Limited
Elsevier
Témata:
Adult
Brain
Brain architecture
Classification
Cognition & reasoning
Cognitive ability
Connectome - methods
Decoding
Female
Fingerprinting
fMRI
Functional anatomy
Functional connectivity
Graph signal processing
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Nervous System Physiological Phenomena
Neuroimaging
Pipelines
Signal processing
Signal Processing, Computer-Assisted
Signatures
Structure-function relationships
Support Vector Machine
Task
Functional connectivity
fMRI
Task
Decoding
Fingerprinting
Graph signal processing
ISSN:1053-8119, 1095-9572, 1095-9572
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Abstract •The relation of brain function with the underlying structural wiring is complex.•We propose new structure-informed graph signal processing (GSP) filtering of functional data.•GSP-derived features allow accurate task decoding and individual fingerprinting.•Functional connectivity from filtered data is more unique to subject and cognition.•The role of structurally aligned and liberal graph frequencies is elucidated. Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.
AbstractList •The relation of brain function with the underlying structural wiring is complex.•We propose new structure-informed graph signal processing (GSP) filtering of functional data.•GSP-derived features allow accurate task decoding and individual fingerprinting.•Functional connectivity from filtered data is more unique to subject and cognition.•The role of structurally aligned and liberal graph frequencies is elucidated. Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.
Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.
Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.
ArticleNumber 118970
Author Van De Ville, Dimitri
Preti, Maria Giulia
Amico, Enrico
Liégeois, Raphaël
Griffa, Alessandra
Author_xml – sequence: 1
  givenname: Alessandra
  surname: Griffa
  fullname: Griffa, Alessandra
  email: alessandra.griffa@epfl.ch
  organization: Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
– sequence: 2
  givenname: Enrico
  surname: Amico
  fullname: Amico, Enrico
  organization: Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland
– sequence: 3
  givenname: Raphaël
  surname: Liégeois
  fullname: Liégeois, Raphaël
  organization: Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland
– sequence: 4
  givenname: Dimitri
  surname: Van De Ville
  fullname: Van De Ville, Dimitri
  organization: Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland
– sequence: 5
  givenname: Maria Giulia
  surname: Preti
  fullname: Preti, Maria Giulia
  organization: Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35124226$$D View this record in MEDLINE/PubMed
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1095-9572
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Keywords Functional connectivity
fMRI
Task
Decoding
Fingerprinting
Graph signal processing
Language English
License This is an open access article under the CC BY-NC-ND license.
Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
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Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and...
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Brain
Brain architecture
Classification
Cognition & reasoning
Cognitive ability
Connectome - methods
Decoding
Female
Fingerprinting
fMRI
Functional anatomy
Functional connectivity
Graph signal processing
Humans
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Magnetic Resonance Imaging - methods
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Nervous System Physiological Phenomena
Neuroimaging
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Signal processing
Signal Processing, Computer-Assisted
Signatures
Structure-function relationships
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