Brain structure-function coupling provides signatures for task decoding and individual fingerprinting
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 an...
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| Vydané v: | bioRxiv |
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| Hlavní autori: | , , , , |
| Médium: | Paper |
| Jazyk: | English |
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Cold Spring Harbor Laboratory
14.11.2021
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| Vydanie: | 1.2 |
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| ISSN: | 2692-8205 |
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| Abstract | 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 potential of GSP to introduce new imaging-based biomarkers to characterize tasks and individuals. 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 structurefunction 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, that is the one decoupled from structure. A network mainly involving cortico-subcortical connections showed the strongest 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.
The relation of brain function with the underlying structural wiring is complex
We propose new structure-informed graph signal processing (GSP) 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 |
|---|---|
| AbstractList | 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 potential of GSP to introduce new imaging-based biomarkers to characterize tasks and individuals. 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 structurefunction 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, that is the one decoupled from structure. A network mainly involving cortico-subcortical connections showed the strongest 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.
The relation of brain function with the underlying structural wiring is complex
We propose new structure-informed graph signal processing (GSP) 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 |
| Author | Van De Ville, Dimitri Preti, Maria Giulia Amico, Enrico Liégeois, Raphaël Griffa, Alessandra |
| Author_xml | – sequence: 1 givenname: Alessandra orcidid: 0000-0003-1923-1653 surname: Griffa fullname: Griffa, Alessandra email: alessandra.griffa@gmail.com organization: Institute of Bioengineering, Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL) – sequence: 2 givenname: Enrico orcidid: 0000-0001-6705-9689 surname: Amico fullname: Amico, Enrico organization: Department of Radiology and Medical Informatics, University of Geneva (UNIGE) – sequence: 3 givenname: Raphaël orcidid: 0000-0003-3985-3898 surname: Liégeois fullname: Liégeois, Raphaël organization: Department of Radiology and Medical Informatics, University of Geneva (UNIGE) – sequence: 4 givenname: Dimitri orcidid: 0000-0002-2879-3861 surname: Van De Ville fullname: Van De Ville, Dimitri organization: CIBM Center for Biomedical Imaging – sequence: 5 givenname: Maria Giulia orcidid: 0000-0002-5323-5327 surname: Preti fullname: Preti, Maria Giulia organization: CIBM Center for Biomedical Imaging |
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| ContentType | Paper |
| Copyright | 2021, Posted by Cold Spring Harbor Laboratory |
| Copyright_xml | – notice: 2021, Posted by Cold Spring Harbor Laboratory |
| DBID | FX. |
| DOI | 10.1101/2021.04.19.440314 |
| DatabaseName | bioRxiv |
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| Discipline | Biology |
| EISSN | 2692-8205 |
| Edition | 1.2 |
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| IngestDate | Tue Jan 07 18:48:53 EST 2025 |
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| Keywords | fMRI task functional connectivity Graph signal processing fingerprinting decoding |
| Language | English |
| License | This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0 |
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| Notes | Competing Interest Statement: The authors have declared no competing interest. |
| ORCID | 0000-0001-6705-9689 0000-0002-5323-5327 0000-0003-3985-3898 0000-0003-1923-1653 0000-0002-2879-3861 |
| OpenAccessLink | https://www.biorxiv.org/content/10.1101/2021.04.19.440314 |
| PageCount | 27 |
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| PublicationCentury | 2000 |
| PublicationDate | 20211114 |
| PublicationDateYYYYMMDD | 2021-11-14 |
| PublicationDate_xml | – month: 11 year: 2021 text: 20211114 day: 14 |
| PublicationDecade | 2020 |
| PublicationTitle | bioRxiv |
| PublicationYear | 2021 |
| Publisher | Cold Spring Harbor Laboratory |
| Publisher_xml | – name: Cold Spring Harbor Laboratory |
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| Title | Brain structure-function coupling provides signatures for task decoding and individual fingerprinting |
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