Uncovering Cortical Units of Processing From Multi-Layered Connectomes
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profi...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-03-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.836259/full |
| _version_ | 1830511376432365568 |
|---|---|
| author | Kristoffer Jon Albers Matthew G. Liptrot Karen Sandø Ambrosen Rasmus Røge Tue Herlau Kasper Winther Andersen Hartwig R. Siebner Hartwig R. Siebner Hartwig R. Siebner Lars Kai Hansen Tim B. Dyrby Tim B. Dyrby Kristoffer H. Madsen Kristoffer H. Madsen Mikkel N. Schmidt Morten Mørup |
| author_facet | Kristoffer Jon Albers Matthew G. Liptrot Karen Sandø Ambrosen Rasmus Røge Tue Herlau Kasper Winther Andersen Hartwig R. Siebner Hartwig R. Siebner Hartwig R. Siebner Lars Kai Hansen Tim B. Dyrby Tim B. Dyrby Kristoffer H. Madsen Kristoffer H. Madsen Mikkel N. Schmidt Morten Mørup |
| author_sort | Kristoffer Jon Albers |
| collection | DOAJ |
| description | Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units. |
| first_indexed | 2024-12-22T02:09:52Z |
| format | Article |
| id | doaj.art-26bde454eef5404d9724120dfd9b517e |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T02:09:52Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-26bde454eef5404d9724120dfd9b517e2022-12-21T18:42:26ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-03-011610.3389/fnins.2022.836259836259Uncovering Cortical Units of Processing From Multi-Layered ConnectomesKristoffer Jon Albers0Matthew G. Liptrot1Karen Sandø Ambrosen2Rasmus Røge3Tue Herlau4Kasper Winther Andersen5Hartwig R. Siebner6Hartwig R. Siebner7Hartwig R. Siebner8Lars Kai Hansen9Tim B. Dyrby10Tim B. Dyrby11Kristoffer H. Madsen12Kristoffer H. Madsen13Mikkel N. Schmidt14Morten Mørup15Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, DenmarkDepartment of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, DenmarkDepartment of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, DenmarkModern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.https://www.frontiersin.org/articles/10.3389/fnins.2022.836259/fullmulti-layered connectomesdMRIfMRIstochastic block modelbrain parcellation |
| spellingShingle | Kristoffer Jon Albers Matthew G. Liptrot Karen Sandø Ambrosen Rasmus Røge Tue Herlau Kasper Winther Andersen Hartwig R. Siebner Hartwig R. Siebner Hartwig R. Siebner Lars Kai Hansen Tim B. Dyrby Tim B. Dyrby Kristoffer H. Madsen Kristoffer H. Madsen Mikkel N. Schmidt Morten Mørup Uncovering Cortical Units of Processing From Multi-Layered Connectomes Frontiers in Neuroscience multi-layered connectomes dMRI fMRI stochastic block model brain parcellation |
| title | Uncovering Cortical Units of Processing From Multi-Layered Connectomes |
| title_full | Uncovering Cortical Units of Processing From Multi-Layered Connectomes |
| title_fullStr | Uncovering Cortical Units of Processing From Multi-Layered Connectomes |
| title_full_unstemmed | Uncovering Cortical Units of Processing From Multi-Layered Connectomes |
| title_short | Uncovering Cortical Units of Processing From Multi-Layered Connectomes |
| title_sort | uncovering cortical units of processing from multi layered connectomes |
| topic | multi-layered connectomes dMRI fMRI stochastic block model brain parcellation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.836259/full |
| work_keys_str_mv | AT kristofferjonalbers uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT matthewgliptrot uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT karensandøambrosen uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT rasmusrøge uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT tueherlau uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT kasperwintherandersen uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT hartwigrsiebner uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT hartwigrsiebner uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT hartwigrsiebner uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT larskaihansen uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT timbdyrby uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT timbdyrby uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT kristofferhmadsen uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT kristofferhmadsen uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT mikkelnschmidt uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes AT mortenmørup uncoveringcorticalunitsofprocessingfrommultilayeredconnectomes |