dc.contributor.author | Bolton, Thomas A.W. | |
dc.contributor.author | Uruñuela Tremiño, Eneko | |
dc.contributor.author | Tian, Ye | |
dc.contributor.author | Zalesky, Andrew | |
dc.contributor.author | Caballero Gaudes, César | |
dc.contributor.author | Van De Ville, Dimitri | |
dc.date.accessioned | 2020-12-03T14:25:40Z | |
dc.date.available | 2020-12-03T14:25:40Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Thomas A W Bolton et al 2020 J. Neural Eng. 17 065003 | es_ES |
dc.identifier.issn | 1741-2560 | |
dc.identifier.uri | http://hdl.handle.net/10810/48772 | |
dc.description | Published 19 November 202 | es_ES |
dc.description.abstract | Accurate mapping of the functional interactions between remote brain areas with resting-state
functional magnetic resonance imaging requires the quantification of their underlying dynamics.
In conventional methodological pipelines, a spatial scale of interest is first selected and dynamic
analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or
states are studied, more local regional rearrangements are then not described, potentially missing
important neurobiological information. Here, we propose a novel mathematical framework that
jointly estimates resting-state functional networks and spatially more localised cross-regional
modulations. To do so, the changes in activity of each brain region are modelled by a logistic
regression including co-activation coefficients (reflective of network assignment, as they highlight
simultaneous activations across areas) and causal interplays (denoting finer regional cross-talks,
when one region active at time t modulates the t to t +1 transition likelihood of another area). A
two-parameter ℓ1 regularisation scheme is used to make these two sets of coefficients sparse: one
controls overall sparsity, while the other governs the trade-off between co-activations and causal
interplays, enabling to properly fit the data despite the yet unknown balance between both types of
couplings. Across a range of simulation settings, we show that the framework successfully retrieves
the two types of cross-regional interactions at once. Performance across noise and sample size
settings was globally on par with that of other existing methods, with the potential to reveal more
precise information missed by alternative approaches. Preliminary application to experimental
data revealed that in the resting brain, co-activations and causal modulations co-exist with a
varying balance across regions. Our methodological pipeline offers a conceptually elegant
alternative for the assessment of functional brain dynamics and can be downloaded at
https://c4science.ch/source/Sparse_logistic_regression.git. | es_ES |
dc.description.sponsorship | Thomas A W Bolton acknowledges the support of the Japan JST ERATO Grant Number JPMJER1801, the Bertarelli Foundation and the Vasco Sanz Fund. Eneko Uruñuela acknowledges the support of the Basque Government Predoctoral fellowship 2020–2024. César Caballero-Gaudes acknowledges the support of the Spanish Ministry of Economy and Competitiveness through the Ramon y Cajal Fellowship (RYC-2017-21845), the Spanish State Research Agency through the BCBL 'Severo Ochoa' excellence accreditation (SEV-2015-490) and the Basque Government through the BERC 2018-2021 program and research project PIBA 19-0104. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Journal of Neural Engineering | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/RYC-2017-21845 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/SEV-2015-0490 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | functional magnetic resonance imaging | es_ES |
dc.subject | dynamic functional connectivity | es_ES |
dc.subject | effective connectivity | es_ES |
dc.subject | sparse coupled logistic regression | es_ES |
dc.subject | ℓ1 regularisation | es_ES |
dc.title | Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2020 The Author(s). Published by IOP Publishing Ltd | es_ES |
dc.relation.publisherversion | https://iopscience.iop.org/journal/1741-2552 | es_ES |
dc.identifier.doi | 10.1088/1741-2552/aba55e | |