Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints
There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past activity in other regions. The coefficients of the...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10129180/ |
| _version_ | 1797814574706065408 |
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| author | Hernan Hernandez Larzabal David Araya Lazara Liset Gonzalez Rodriguez Claudio Roman Nelson Trujillo-Barreto Pamela Guevara Wael El-Deredy |
| author_facet | Hernan Hernandez Larzabal David Araya Lazara Liset Gonzalez Rodriguez Claudio Roman Nelson Trujillo-Barreto Pamela Guevara Wael El-Deredy |
| author_sort | Hernan Hernandez Larzabal |
| collection | DOAJ |
| description | There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past activity in other regions. The coefficients of the MAR are taken as estimates of effective brain connectivity. However, assumption of stationarity, and the large number of coefficients renders the MAR impractical for estimating brain networks from standard neuroimaging time-series of limited durations. We propose HsMM-MAR-AC, a novel sparse hybrid discrete-continuous model for the efficient estimation of time-dependent effective brain networks from non-stationary brain activity time-series. Discrete quasi-stationary Brain States, and the fast switching between them, are modelled by a Hidden semi-Markov Model whose continuous emissions are drawn from a sparse MAR. The coefficients of the MAR are restricted by Anatomical Brain Connectivity information in two ways: 1) Effective direct connectivity between two brain regions is only considered if the corresponding anatomical connection exists; and 2) the autoregressors lag associated with each connection is based on the fiber length between the two regions, such that only one lag per connection is estimated. We test the accuracy of HsMM-MAR-AC in recovering simulated resting state networks of various durations, and at different thresholds of anatomical restrictions. We demonstrate that HsMM-MAR-AC recovers the RSNs more accurately than the benchmark method of the sliding window, with as little as 4 minutes of data. We also show that when the anatomical restrictions are relaxed, longer time-series are needed to estimate the networks, and became computationally unfeasible without anatomical restrictions. HsMM-MAR-AC offers an efficient model for estimating time-dependent Effective Connectivity from neuroimaging data that exploits the advantages of Hidden Markov and MAR models without identifiability problems, excessive demand on data collection, or unnecessary computational effort. |
| first_indexed | 2024-03-13T08:09:42Z |
| format | Article |
| id | doaj.art-a626f91ba1e5445d9f6eb6ef2f28acff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T08:09:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a626f91ba1e5445d9f6eb6ef2f28acff2023-05-31T23:00:38ZengIEEEIEEE Access2169-35362023-01-0111502155023410.1109/ACCESS.2023.327773110129180Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity ConstraintsHernan Hernandez Larzabal0https://orcid.org/0000-0002-0597-6059David Araya1Lazara Liset Gonzalez Rodriguez2https://orcid.org/0000-0002-2234-3544Claudio Roman3Nelson Trujillo-Barreto4https://orcid.org/0000-0001-6581-7503Pamela Guevara5Wael El-Deredy6https://orcid.org/0000-0002-9822-1092Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Concepción, ChileFacultad de Ingeniería, Universidad Andrés Bello, Santiago, ChileDepartamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Concepción, ChileDepartamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Concepción, ChileManchester Academic Health Science Centre, Faculty of Biology, Medicine and Health, School of Health Sciences, The University of Manchester, Manchester, U.KDepartamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Concepción, ChileCentro de Investigación y Desarrollo en Ingenier\'ia en Salud, Universidad de Valparaíso, Valparaíso, ChileThere is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past activity in other regions. The coefficients of the MAR are taken as estimates of effective brain connectivity. However, assumption of stationarity, and the large number of coefficients renders the MAR impractical for estimating brain networks from standard neuroimaging time-series of limited durations. We propose HsMM-MAR-AC, a novel sparse hybrid discrete-continuous model for the efficient estimation of time-dependent effective brain networks from non-stationary brain activity time-series. Discrete quasi-stationary Brain States, and the fast switching between them, are modelled by a Hidden semi-Markov Model whose continuous emissions are drawn from a sparse MAR. The coefficients of the MAR are restricted by Anatomical Brain Connectivity information in two ways: 1) Effective direct connectivity between two brain regions is only considered if the corresponding anatomical connection exists; and 2) the autoregressors lag associated with each connection is based on the fiber length between the two regions, such that only one lag per connection is estimated. We test the accuracy of HsMM-MAR-AC in recovering simulated resting state networks of various durations, and at different thresholds of anatomical restrictions. We demonstrate that HsMM-MAR-AC recovers the RSNs more accurately than the benchmark method of the sliding window, with as little as 4 minutes of data. We also show that when the anatomical restrictions are relaxed, longer time-series are needed to estimate the networks, and became computationally unfeasible without anatomical restrictions. HsMM-MAR-AC offers an efficient model for estimating time-dependent Effective Connectivity from neuroimaging data that exploits the advantages of Hidden Markov and MAR models without identifiability problems, excessive demand on data collection, or unnecessary computational effort.https://ieeexplore.ieee.org/document/10129180/Anatomical constraintbrain statehidden semi Markov modelmultivariate autoregressive modelstate duration |
| spellingShingle | Hernan Hernandez Larzabal David Araya Lazara Liset Gonzalez Rodriguez Claudio Roman Nelson Trujillo-Barreto Pamela Guevara Wael El-Deredy Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints IEEE Access Anatomical constraint brain state hidden semi Markov model multivariate autoregressive model state duration |
| title | Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints |
| title_full | Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints |
| title_fullStr | Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints |
| title_full_unstemmed | Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints |
| title_short | Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints |
| title_sort | efficient estimation of time dependent brain functional connectivity using anatomical connectivity constraints |
| topic | Anatomical constraint brain state hidden semi Markov model multivariate autoregressive model state duration |
| url | https://ieeexplore.ieee.org/document/10129180/ |
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