Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data
Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity o...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-11-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921006832 |
| _version_ | 1819089880051875840 |
|---|---|
| author | Arun S. Mahadevan Ursula A. Tooley Maxwell A. Bertolero Allyson P. Mackey Danielle S. Bassett |
| author_facet | Arun S. Mahadevan Ursula A. Tooley Maxwell A. Bertolero Allyson P. Mackey Danielle S. Bassett |
| author_sort | Arun S. Mahadevan |
| collection | DOAJ |
| description | Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study. |
| first_indexed | 2024-12-21T22:14:57Z |
| format | Article |
| id | doaj.art-2e0461fc191e47608861f17880fb1be1 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-21T22:14:57Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-2e0461fc191e47608861f17880fb1be12022-12-21T18:48:29ZengElsevierNeuroImage1095-95722021-11-01241118408Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI dataArun S. Mahadevan0Ursula A. Tooley1Maxwell A. Bertolero2Allyson P. Mackey3Danielle S. Bassett4Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USANeuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA 19104, USADepartment of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Psychology, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA; Corresponding author at: Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.http://www.sciencedirect.com/science/article/pii/S1053811921006832Functional connectivityCorrelationCoherence, Mutual informationResting-stateMotion |
| spellingShingle | Arun S. Mahadevan Ursula A. Tooley Maxwell A. Bertolero Allyson P. Mackey Danielle S. Bassett Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data NeuroImage Functional connectivity Correlation Coherence, Mutual information Resting-state Motion |
| title | Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data |
| title_full | Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data |
| title_fullStr | Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data |
| title_full_unstemmed | Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data |
| title_short | Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data |
| title_sort | evaluating the sensitivity of functional connectivity measures to motion artifact in resting state fmri data |
| topic | Functional connectivity Correlation Coherence, Mutual information Resting-state Motion |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921006832 |
| work_keys_str_mv | AT arunsmahadevan evaluatingthesensitivityoffunctionalconnectivitymeasurestomotionartifactinrestingstatefmridata AT ursulaatooley evaluatingthesensitivityoffunctionalconnectivitymeasurestomotionartifactinrestingstatefmridata AT maxwellabertolero evaluatingthesensitivityoffunctionalconnectivitymeasurestomotionartifactinrestingstatefmridata AT allysonpmackey evaluatingthesensitivityoffunctionalconnectivitymeasurestomotionartifactinrestingstatefmridata AT daniellesbassett evaluatingthesensitivityoffunctionalconnectivitymeasurestomotionartifactinrestingstatefmridata |