HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Brain topography beyond parcellations: local gradients of functional maps

Abstract : Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data —concretely, the prediction of task-fMRI from rest-fMRI maps across subjects— we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations —as opposed to a single fixed parcellation— and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/hal-03122173
Contributor : Bertrand Thirion Connect in order to contact the contributor
Submitted on : Wednesday, May 19, 2021 - 10:00:06 PM
Last modification on : Thursday, February 3, 2022 - 3:09:00 AM

File

paper.pdf
Files produced by the author(s)

Identifiers

Citation

Elvis Dohmatob, Hugo Richard, Ana Luísa Pinho, Bertrand Thirion. Brain topography beyond parcellations: local gradients of functional maps. NeuroImage, Elsevier, 2021, pp.117706. ⟨10.1016/j.neuroimage.2020.117706⟩. ⟨hal-03122173v2⟩

Share

Metrics

Record views

493

Files downloads

132