Directed functional connectivity using dynamic graphical models
There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2018
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| _version_ | 1797092574883741696 |
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| author | Schwab, S Harbord, R Zerbi, V Elliott, L Afyouni, S Smith, J Woolrich, M Smith, S Nichols, T |
| author_facet | Schwab, S Harbord, R Zerbi, V Elliott, L Afyouni, S Smith, J Woolrich, M Smith, S Nichols, T |
| author_sort | Schwab, S |
| collection | OXFORD |
| description | There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R. |
| first_indexed | 2024-03-07T03:47:55Z |
| format | Journal article |
| id | oxford-uuid:c0292ffd-d241-4c50-99ef-9248fcea9d01 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:47:55Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:c0292ffd-d241-4c50-99ef-9248fcea9d012022-03-27T05:52:38ZDirected functional connectivity using dynamic graphical modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c0292ffd-d241-4c50-99ef-9248fcea9d01EnglishSymplectic Elements at OxfordElsevier2018Schwab, SHarbord, RZerbi, VElliott, LAfyouni, SSmith, JWoolrich, MSmith, SNichols, TThere are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R. |
| spellingShingle | Schwab, S Harbord, R Zerbi, V Elliott, L Afyouni, S Smith, J Woolrich, M Smith, S Nichols, T Directed functional connectivity using dynamic graphical models |
| title | Directed functional connectivity using dynamic graphical models |
| title_full | Directed functional connectivity using dynamic graphical models |
| title_fullStr | Directed functional connectivity using dynamic graphical models |
| title_full_unstemmed | Directed functional connectivity using dynamic graphical models |
| title_short | Directed functional connectivity using dynamic graphical models |
| title_sort | directed functional connectivity using dynamic graphical models |
| work_keys_str_mv | AT schwabs directedfunctionalconnectivityusingdynamicgraphicalmodels AT harbordr directedfunctionalconnectivityusingdynamicgraphicalmodels AT zerbiv directedfunctionalconnectivityusingdynamicgraphicalmodels AT elliottl directedfunctionalconnectivityusingdynamicgraphicalmodels AT afyounis directedfunctionalconnectivityusingdynamicgraphicalmodels AT smithj directedfunctionalconnectivityusingdynamicgraphicalmodels AT woolrichm directedfunctionalconnectivityusingdynamicgraphicalmodels AT smiths directedfunctionalconnectivityusingdynamicgraphicalmodels AT nicholst directedfunctionalconnectivityusingdynamicgraphicalmodels |