Automated Mapping of Sensorimotor Network for Resting State fMRI Data with Seed-Based Correlation Analysis
| Autor: | Alexandre Rosa Franco, Dario F. G. de Azevedo, Nathalia Bianchini Esper, José Osmar Alves Filho, Bruno Goulart de Oliveira |
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| Rok vydání: | 2019 |
| Předmět: |
Data processing
Resting state fMRI medicine.diagnostic_test Computer science business.industry Pattern recognition Task (project management) Correlation Identification (information) Similarity (network science) Sensorimotor network medicine Artificial intelligence business Functional magnetic resonance imaging |
| Zdroj: | XXVI Brazilian Congress on Biomedical Engineering ISBN: 9789811325168 |
| DOI: | 10.1007/978-981-13-2517-5_81 |
| Popis: | An algorithm for automated placement of regions of interest (ROI) in Seed Based Correlation (SBC) data analysis for resting-state functional Magnetic Resonance Imaging (rs-fMRI) is presented in this paper. The sensorimotor network was used for testing and validation. Most of the available literature shows the use of manual seed selection in order to find the Resting-State Networks (RSNs). Typically, a seed is placed in the most preserved side of brain and its functional connectivity (correlation) with the contra-lateral hemisphere allows the identification of the network within the lesioned side of the brain. The manual placement of the seeds is usually a laborious task and prone to human error. The developed algorithm was based on the automated spatial registration of an atlas to the space of the patient’s brain: Anatomical (HarvardOxford) and functional (Brodmann Areas) atlas. Regions of interest representing the sensorimotor networks were used as seeds. FMRI data from 8 healthy volunteers were used to assess its validation. These data included a finger-tapping task and a resting-state protocol. The extracted sensorimotor RSNs derived from the automated procedure were compared to the task-based fMRI maps and RSNs extracted from SBC with manual ROI placement. Preliminary results show a good level of similarity between seed-based and task-based motor network maps, except in one case in which the patterns did not match. This technique shows potential to be used in clinical application due to the automated nature of the data processing as well as the ease for patients to perform the exam. |
| Databáze: | OpenAIRE |
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