Open AccessEstablishing an acquisition and processing protocol for resting state networks with a 1.5 T scanner
Author(s) -
Michela Moreno-Ayure,
Cristian Páez,
María A López-Arias,
Johan L Mendez-Betancurt,
Edgar G. Ordóñez-Rubiano,
Jorge Rudas,
Cristian Pulido,
Francisco Gómez,
Darwin Martínez,
César O. Enciso-Olivera,
Diana Rivera-Triana,
Rosángela Casanova-Libreros,
Natalia Aguilera,
Jorge H. Marín-Muñoz
Publication year - 2020
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000021125
Subject(s) - scanner , default mode network , functional magnetic resonance imaging , artifact (error) , resting state fmri , medicine , blood oxygen level dependent , artificial intelligence , computer science , radiology
Objective: The aim of this study was to characterize the capability of detection of the resting state networks (RSNs) with functional magnetic resonance imaging (fMRI) in healthy subjects using a 1.5T scanner in a middle-income country. Materials and methods: Ten subjects underwent a complete blood-oxygen-level dependent imaging (BOLD) acquisition on a 1.5T scanner. For the imaging analysis, we used the spatial independent component analysis (sICA). We designed a computer tool for 1.5 T (or above) scanners for imaging processing. We used it to separate and delineate the different components of the RSNs of the BOLD signal. The sICA was also used to differentiate the RSNs from noise artifact generated by breathing and cardiac cycles. Results: For each subject, 20 independent components (IC) were computed from the sICA (a total of 200 ICs). From these ICs, a spatial pattern consistent with RSNs was identified in 161 (80.5%). From the 161, 131 (65.5%) were fit for study. The networks that were found in all subjects were: the default mode network, the right executive control network, the medial visual network, and the cerebellar network. In 90% of the subjects, the left executive control network and the sensory/motor network were observed. The occipital visual network was present in 80% of the subjects. In 39 (19.5%) of the images, no any neural network was identified. Conclusions: Reproduction and differentiation of the most representative RSNs was achieved using a 1.5T scanner acquisitions and sICA processing of BOLD imaging in healthy subjects.