Joint Modeling of Anatomical and Functional Connectivity for Population Studies
We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and func...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133461 |
| _version_ | 1826202665206415360 |
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| author | Venkataraman, A Rathi, Y Kubicki, M Westin, C Golland, P |
| author_facet | Venkataraman, A Rathi, Y Kubicki, M Westin, C Golland, P |
| author_sort | Venkataraman, A |
| collection | MIT |
| description | We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation. © 2011 IEEE. |
| first_indexed | 2024-09-23T12:13:12Z |
| format | Article |
| id | mit-1721.1/133461 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:13:12Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1334612022-04-01T17:10:41Z Joint Modeling of Anatomical and Functional Connectivity for Population Studies Venkataraman, A Rathi, Y Kubicki, M Westin, C Golland, P We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation. © 2011 IEEE. 2021-10-27T19:52:58Z 2021-10-27T19:52:58Z 2012 2019-05-29T17:16:16Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133461 en 10.1109/TMI.2011.2166083 IEEE Transactions on Medical Imaging Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PMC |
| spellingShingle | Venkataraman, A Rathi, Y Kubicki, M Westin, C Golland, P Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title | Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title_full | Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title_fullStr | Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title_full_unstemmed | Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title_short | Joint Modeling of Anatomical and Functional Connectivity for Population Studies |
| title_sort | joint modeling of anatomical and functional connectivity for population studies |
| url | https://hdl.handle.net/1721.1/133461 |
| work_keys_str_mv | AT venkataramana jointmodelingofanatomicalandfunctionalconnectivityforpopulationstudies AT rathiy jointmodelingofanatomicalandfunctionalconnectivityforpopulationstudies AT kubickim jointmodelingofanatomicalandfunctionalconnectivityforpopulationstudies AT westinc jointmodelingofanatomicalandfunctionalconnectivityforpopulationstudies AT gollandp jointmodelingofanatomicalandfunctionalconnectivityforpopulationstudies |