Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-01-003 ISMRM Abstract

Sensitivity and Specificity Comparison of Real-Time and Offline Resting-State fMRI Analysis Pipelines using ROC Analysis

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Analysis Methods - Software Tools
364-01-003 · fMRI: Analyses · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row E
Keywords: fMRI Analysis Comparative Study Human brain Real-time Resting state fMRI
Accepted
Arthur Schoen1, Logan Dowdle2, Orrin Myers3, Essa Yacoub4,5, Stefan Posse 6
1Department of Electrical Engineering, University of New Mexico, Albuquerque, United States of America
2Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
3Department of Family & Community Medicine, University of New Mexico, Albuquerque, United States of America
4Center for Magnetic Resonance Research, Dept. of Radiology, University of Minnesota, Minneapolis, United States of America
5Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, United States of America
6Neurology and Physics and Astronomy, University of New Mexico, Albuquerque, United States of America
Presenting Author: Stefan Posse

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. 1. Li, R., et al., Large-scale directional connections among multi resting-state neural networks in human brain: a functional MRI and Bayesian network modeling study. Neuroimage, 2011. 56(3): p. 1035-42.
2. 2. De Luca, M., et al., fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage, 2006. 29(4): p. 1359-67.
3. 3. Fox, M.D., et al., The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A, 2005. 102(27): p. 9673-8.
4. 4. Raichle, M.E. and A.Z. Snyder, A default mode of brain function: a brief history of an evolving idea. Neuroimage, 2007. 37(4): p. 1083-90; discussion 1097-9.
5. 5. Schopf, V., et al., Group ICA of resting-state data: a comparison. MAGMA, 2010. 23(5-6): p. 317-25.
6. 6. Abou-Elseoud, A., et al., The effect of model order selection in group PICA. Hum Brain Mapp, 2010. 31(8): p. 1207-16.
7. 7. Allen, E.A., et al., A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci, 2011. 5: p. 2.
8. 8. Smith, S.M., et al., Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A, 2009. 106(31): p. 13040-5.
9. 9. Mannfolk, P., et al., Can resting-state functional MRI serve as a complement to task-based mapping of sensorimotor function? A test-retest reliability study in healthy volunteers. Journal of Magnetic Resonance Imaging, 2011.
10. 10. Zhang, D., et al., Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with functional magnetic resonance imaging: initial experience. Neurosurgery, 2009. 65(6 Suppl): p. 226-36.
11. 11. Lee, M.H., C.D. Smyser, and J.S. Shimony, Resting-State fMRI: A Review of Methods and Clinical Applications. AJNR Am J Neuroradiol, 2012.
12. 12. Liu, H., et al., Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity. Journal of Neurosurgery, 2009. 111(4): p. 746-54.
13. 13. Castellano, A., et al., Functional MRI for Surgery of Gliomas. Curr Treat Options Neurol, 2017. 19(10): p. 34.
14. 14. Lemee, J.M., et al., Resting-state functional magnetic resonance imaging versus task-based activity for language mapping and correlation with perioperative cortical mapping. Brain Behav, 2019. 9(10): p. e01362.
15. 15. Huang, H., et al., PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI. Neuroinformatics, 2016. 14(4): p. 421-38.
16. 16. Hsu, A.L., et al., IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies. Front Neuroinform, 2018. 12: p. 11.
17. 17. Kumar, V.A., et al., Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy. AJNR Am J Neuroradiol, 2023.
18. 18. Cox, R.W. and J.S. Hyde, Software tools for analysis and visualization of fMRI data. NMR Biomed, 1997. 10(4-5): p. 171-8.
19. 19. Whitfield-Gabrieli, S. and A. Nieto-Castanon, Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2012. 2(3): p. 125-41.
20. 20. Catalino, M.P., et al., Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging. Neurosurg Focus, 2020. 48(2): p. E9.
21. 21. Vakamudi, K., et al., Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals. Brain Connect, 2020. 10(8): p. 448-463.
22. 22. Vakamudi, K., et al., Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping. Hum Brain Mapp, 2020. 41(3): p. 797-814.
23. Taylor, P.A., et al., Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage, 2023. 274: p. 120138.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-01-003