Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
304-04-010 ISMRM Abstract

Quantifying Arousal-Driven Variability in fMRI Using Eye Closure Metrics and Connectivity Analyses

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Brain Function and fMRI - Functional Connectivity
304-04-010 · Multimodal and Innovative Design · Monday, 11 May, 4:10 PM–6:00 PM · Ballroom East
Keywords: fMRI (resting state) Functional Connectivity Eye Tracking Multimodal neuroimaging Autonomic Nervous System
Accepted
Elif Can 1, Kadir Berat YILDIRIM2, Kübra Eren1, Cem Karakuzu1, Belal TAVASHI1, Lina Mahmoud Saleh Alqam1, Şirin Yağmur Abacı1, Sinem Aytaç3, Fatmatüzzehra Uçal1, Alp Dinçer4, Pinar S Özbay1
1Boğaziçi University Institute of Biomedical Engineering, Istanbul, Turkey
2TU Delft, Delft, Netherlands
3Department of Psychology, Bogazici University, Istanbul, Turkey
4Department of Radiology, Acibadem University, Istanbul, Turkey
Presenting Author: Elif Can

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. Chang, C., Leopold, D. A., Schölvinck, M. L., Mandelkow, H., Picchioni, D., Liu, X., Ye, F. Q., Turchi, J. N., & Duyn, J. H. (2016). Tracking brain arousal fluctuations with fMRI. Proceedings of the National Academy of Sciences of the United States of America, 113(16), 4518–4523. doi: 10.1073/pnas.1520613113. [doi]
2. Mazziotti, R., Carrara, F., Viglione, A., Lupori, L., Lo Verde, L., Benedetto, A., Ricci, G., Sagona, G., Amato, G., & Pizzorusso, T. (2021). Meye: Web app for translational and real-time pupillometry. eNeuro, 8(5), ENEURO.0122-21.2021. doi: 10.1523/ENEURO.0122-21.2021. [doi]
3. Abe, T. (2023). PERCLOS-based technologies for detecting drowsiness: current evidence and future directions. Sleep Advances, 4(1), zpad006. doi: 10.1093/sleepadvances/zpad006. [doi]
4. Celecia, A., Figueiredo, K., Vellasco, M., & Gonzalez, R. (2020). A portable fuzzy driver drowsiness estimation system. Sensors, 20(15), 4093. doi: 10.3390/s20154093 [doi]
5. Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29(3), 162–173. doi: 10.1006/cbmr.1996.0014. PMID: 8812068. [doi] [pmid]
6. Glover, G. H., Li, T.-Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 162–167. doi: 10.1002/1522-2594(200007)44:1<162::aid-mrm23>3.0.co;2-e [doi]
7. Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J., & Joliot, M. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206, 116189. doi: 10.1016/j.neuroimage.2019.116189. [doi]
8. Bolt, T., Wang, S., Nomi, J. S., Setton, R., Gold, B. P., deB. Frederick, B., Yeo, B. T. T., Chen, J. J., Picchioni, D., Duyn, J. H., Spreng, R. N., Keilholz, S. D., Uddin, L. Q., & Chang, C. (2025). Autonomic physiological coupling of the global fMRI signal. Nature Neuroscience, 28(6), 1327–1335. doi: 10.1101/2023.01.19.524818. [doi]
9. Chang, C., Cunningham, J. P., & Glover, G. H. (2009). Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage, 44(3), 857–869. doi: 10.1016/j.neuroimage.2008.09.029. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/304-04-010