Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data
With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that quantify the human brain connectome. However, there is a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain,. In this paper, we have tested seve...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2015-02-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00048/full |
| _version_ | 1819018366983077888 |
|---|---|
| author | Nathassia Kadletz Aurich José Osmar Alves Filho Ana Maria Marques da Silva Ana Maria Marques da Silva Ana Maria Marques da Silva Alexandre Rosa Franco Alexandre Rosa Franco Alexandre Rosa Franco |
| author_facet | Nathassia Kadletz Aurich José Osmar Alves Filho Ana Maria Marques da Silva Ana Maria Marques da Silva Ana Maria Marques da Silva Alexandre Rosa Franco Alexandre Rosa Franco Alexandre Rosa Franco |
| author_sort | Nathassia Kadletz Aurich |
| collection | DOAJ |
| description | With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that quantify the human brain connectome. However, there is a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain,. In this paper, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different schemes were tested on a publicly available dataset with rs-fMRI of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that with the use of censoring based on outliers within the functional time-series, results indicate an increase in reliability of GT measurements with a reduction in dependency with head motion. |
| first_indexed | 2024-12-21T03:18:17Z |
| format | Article |
| id | doaj.art-5ba0c915b8ae4fd888c916cd02efefd9 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-21T03:18:17Z |
| publishDate | 2015-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-5ba0c915b8ae4fd888c916cd02efefd92022-12-21T19:17:46ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2015-02-01910.3389/fnins.2015.00048103902Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI dataNathassia Kadletz Aurich0José Osmar Alves Filho1Ana Maria Marques da Silva2Ana Maria Marques da Silva3Ana Maria Marques da Silva4Alexandre Rosa Franco5Alexandre Rosa Franco6Alexandre Rosa Franco7PUCRSPUCRSPUCRSPUCRSPUCRSPUCRSPUCRSPUCRSWith resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that quantify the human brain connectome. However, there is a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain,. In this paper, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different schemes were tested on a publicly available dataset with rs-fMRI of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that with the use of censoring based on outliers within the functional time-series, results indicate an increase in reliability of GT measurements with a reduction in dependency with head motion.http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00048/fullfunctional MRIresting stategraph theoryReliabilitypreprocessing |
| spellingShingle | Nathassia Kadletz Aurich José Osmar Alves Filho Ana Maria Marques da Silva Ana Maria Marques da Silva Ana Maria Marques da Silva Alexandre Rosa Franco Alexandre Rosa Franco Alexandre Rosa Franco Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data Frontiers in Neuroscience functional MRI resting state graph theory Reliability preprocessing |
| title | Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data |
| title_full | Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data |
| title_fullStr | Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data |
| title_full_unstemmed | Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data |
| title_short | Evaluating the Reliability of Different Preprocessing Steps to Estimate Graph Theoretical Measures in Resting State fMRI data |
| title_sort | evaluating the reliability of different preprocessing steps to estimate graph theoretical measures in resting state fmri data |
| topic | functional MRI resting state graph theory Reliability preprocessing |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00048/full |
| work_keys_str_mv | AT nathassiakadletzaurich evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT joseosmaralvesfilho evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT anamariamarquesdasilva evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT anamariamarquesdasilva evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT anamariamarquesdasilva evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT alexandrerosafranco evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT alexandrerosafranco evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata AT alexandrerosafranco evaluatingthereliabilityofdifferentpreprocessingstepstoestimategraphtheoreticalmeasuresinrestingstatefmridata |