Prefiltering based on experimental paradigm for analysis of fMRI complex brain networks

Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were perf...

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Detalles Bibliográficos
Autores: Jiménez, Salvador, Rotger, Laura, Aguirre Maeso, Carlos, Muñoz, Alberto, Granados, Sergio, Tornero, Jesús
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/695786
Acceso en línea:http://hdl.handle.net/10486/695786
https://dx.doi.org/10.1371/journal.pone.0238994
Access Level:acceso abierto
Palabra clave:Brain networks
Functional magnetic resonance images
fMRI paradigm
Standard voxel-voxel correlation
Informática
Descripción
Sumario:Brain networks offers a new insight about connections between function and anatomical regions of human brain. We present results from brain networks built from functional magnetic resonance images during finger tapping paradigm. Pearson voxel-voxel correlation in time and frequency domains were performed for all subjects. Besides this standard framework we have implemented a new approach consisting in filtering the data with respect to the fMRI paradigm (finger tapping) in order to obtain a better understanding of the network involved in the execution of the task. The main topological graph measures have been compared in both cases: Voxel-voxel correlation and voxel-paradigm filtering plus voxel-voxel correlation. With the standard voxel-voxel correlation a clearly free-scale network was obtained. On the other hand, when we prefiltered the paradigm we obtained two different kind of networks: 1) free-scale; 2) random-like. To our best knowledge, this behaviour is reported here for first time for brain networks. We suggest that paradigm signal prefiltering can provide more infomation about the brain networks.