Emergence of canonical functional networks from the structural connectome
How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmis...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921004675 |
| _version_ | 1818621930750607360 |
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| author | Xihe Xie Chang Cai Pablo F. Damasceno Srikantan S. Nagarajan Ashish Raj |
| author_facet | Xihe Xie Chang Cai Pablo F. Damasceno Srikantan S. Nagarajan Ashish Raj |
| author_sort | Xihe Xie |
| collection | DOAJ |
| description | How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI. |
| first_indexed | 2024-12-16T18:17:06Z |
| format | Article |
| id | doaj.art-1b80db003cd84e44b8db6379af2fa66d |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-16T18:17:06Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-1b80db003cd84e44b8db6379af2fa66d2022-12-21T22:21:39ZengElsevierNeuroImage1095-95722021-08-01237118190Emergence of canonical functional networks from the structural connectomeXihe Xie0Chang Cai1Pablo F. Damasceno2Srikantan S. Nagarajan3Ashish Raj4Department of Neuroscience, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10028, United StatesDepartment of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United StatesCenter for Intelligent Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA, United StatesDepartment of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States; Corresponding authors.Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, United States; Corresponding authors.How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.http://www.sciencedirect.com/science/article/pii/S1053811921004675Structural connectivityFunctional networksGraph LaplacianComplex Laplacian |
| spellingShingle | Xihe Xie Chang Cai Pablo F. Damasceno Srikantan S. Nagarajan Ashish Raj Emergence of canonical functional networks from the structural connectome NeuroImage Structural connectivity Functional networks Graph Laplacian Complex Laplacian |
| title | Emergence of canonical functional networks from the structural connectome |
| title_full | Emergence of canonical functional networks from the structural connectome |
| title_fullStr | Emergence of canonical functional networks from the structural connectome |
| title_full_unstemmed | Emergence of canonical functional networks from the structural connectome |
| title_short | Emergence of canonical functional networks from the structural connectome |
| title_sort | emergence of canonical functional networks from the structural connectome |
| topic | Structural connectivity Functional networks Graph Laplacian Complex Laplacian |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921004675 |
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