Multi-modal correlates of fMRI-identified co-activation patterns
Poster No:
1093
Submission Type:
Abstract Submission
Authors:
Karl-Heinz Nenning1, Samuel Louviot1, Arielle Tambini1, Eduardo Gonzalez-Moreira1, Ting Xu2, Hyun-Woong Kim1, Teddy Hoppe1, Jenna Lembo1, Takuya Ito3, Stanley Colcombe1, Alexandre Franco1, Michael Milham2
Institutions:
1Nathan Kline Institute, Orangeburg, NY, 2Child Mind Institute, New York, NY, 3IBM Research, Yorktown Heights, NY
First Author:
Co-Author(s):
Introduction:
Functional magnetic resonance imaging (fMRI) is a powerful tool to track whole-brain spatio-temporal signatures of dynamic brain activity, often referred to as brain states (Greene et al., 2023). Previous work has shown that such fMRI-identified brain states are related to fluctuations in ongoing behavior and may be a promising marker in health and disease (Cai et al., 2021; Marshall et al., 2020). However, collecting high-quality fMRI data has practical challenges regarding cost, the need for extensive infrastructure, or compliance in patient populations, fundamentally limiting its utility, especially at large scales. In contrast, readily available and less expensive technologies such as eye tracking or electroencephalography (EEG) are better suited for large-scale data collection and clinical applications. Here, we aim to bridge the gap between sensitive fMRI markers and more accessible physiological measures by examining the utility of non-fMRI-based signals such as eye tracking and EEG to characterize and predict fMRI-identified brain states.
Methods:
We analyzed an openly available dataset that includes simultaneously acquired fMRI, EEG, and eye tracking data from 22 individuals across two sessions, including resting-state, flickering checkerboard, and naturalistic movie viewing tasks (Telesford et al., 2023). This allowed us to characterize brain states across multiple stimulus-driven conditions and task-free states. We used co-activation pattern (CAP) analysis to establish fMRI-identified brain states across the task conditions via temporal clustering (Liu et al., 2018). We first examined whether the strength of each CAP (i.e. graded measure of brain state) correlated reliably with non-fMRI signals (i.e. pupil dilation, PD) at varying temporal lags. We next adapted a previously introduced regression framework to establish eye tracking and EEG predictors of time-varying fMRI-identified brain states (Meir-Hasson et al., 2014, 2016). The fMRI-identified CAP strength was used as a target in the regression framework, and for each time-point, non-fMRI features within a preceding time-window (12 sec) were used to train the coefficients of the prediction model. We performed cross-subject prediction (i.e. leave one participant out) to test the generalizability of non-fMRI predictors, and quantified performance with Pearson's correlation between predicted and observed data.
Results:
Temporal clustering of the fMRI data yielded 4 pairs of CAPs, comprising distinct modes of time-varying brain states: one CAP pair strongly weighted the visual network (CAPs 1-2), another reflected internal vs. external focus (differentially weighting the default mode vs. dorsal attention network, CAPs 3-4), and others weighted frontoparietal networks (CAPs 5-8). We found a strong relationship between PD and visual CAPs during the checkerboard stimulus and a strong association between PD and higher-order CAPs during resting-state and movie watching (Figure 1a). In addition, the cross-subject regression framework revealed that PD was predictive for visual CAPs during the checkerboard stimulus (r>0.3), and the frontoparietal CAP during rest (r>0.3) and movie watching (r>0.25). Preliminary results with EEG data included in the CAP prediction model indicated an increase in predictive performance, primarily for higher-order CAPs.
Conclusions:
Our findings suggest the feasibility of leveraging readily available physiological measures such as eye tracking and EEG to predict fMRI markers of dynamic brain states. A generalizable model could have practical implications to enhance the value of more accessible physiological measures. Further research is necessary to identify the most sensitive non-fMRI features and their optimal predictive potential.
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Other Methods 2
Keywords:
Other - eye tracking; EEG-fMRI; co-activation patterns
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Greene, A. S., Horien, C., Barson, D., Scheinost, D., & Todd Constable, R. (2023). Why is everyone talking about brain state? Trends in Neurosciences, 46(7), 508–524.
Liu, X., Zhang, N., Chang, C., & Duyn, J. H. (2018). Co-activation patterns in resting-state fMRI signals. NeuroImage, 180(Pt B). https://doi.org/10.1016/j.neuroimage.2018.01.041
Marshall, E., Nomi, J. S., Dirks, B., Romero, C., Kupis, L., Chang, C., & Uddin, L. Q. (2020). Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder. Network Neuroscience, 4(4), 1219–1234.
Meir-Hasson, Y., Keynan, J. N., Kinreich, S., Jackont, G., Cohen, A., Podlipsky-Klovatch, I., Hendler, T., & Intrator, N. (2016). One-Class FMRI-Inspired EEG Model for Self-Regulation Training. PloS One, 11(5), e0154968.
Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., & Intrator, N. (2014). An EEG Finger-Print of fMRI deep regional activation. NeuroImage, 102 Pt 1. https://doi.org/10.1016/j.neuroimage.2013.11.004
Telesford, Q. K., Gonzalez-Moreira, E., Xu, T., Tian, Y., Colcombe, S. J., Cloud, J., Russ, B. E., Falchier, A., Nentwich, M., Madsen, J., Parra, L. C., Schroeder, C. E., Milham, M. P., & Franco, A. R. (2023). An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI. Scientific Data, 10(1), 1–13.