Relationship between spontaneous events in rsfMRI and underlying electrophysiological activities
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1659
Submission Type:
Abstract Submission
Authors:
Rozhan Salehi1, Will Wilson2,3, Paolo Federico2,3,4,5, Pierre LeVan2,5
Institutions:
1University of Calgary, Calgary, Canada, 2Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada, 3Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Canada, 4Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada, 5Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada
First Author:
Co-Author(s):
Will Wilson
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Seaman Family MR Research Centre, Foothills Medical Centre
Calgary, Canada|Calgary, Canada
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Seaman Family MR Research Centre, Foothills Medical Centre
Calgary, Canada|Calgary, Canada
Paolo Federico
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Seaman Family MR Research Centre, Foothills Medical Centre|Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Seaman Family MR Research Centre, Foothills Medical Centre|Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada|Calgary, Canada|Calgary, Canada
Pierre LeVan
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary|Department of Radiology, Cumming School of Medicine, University of Calgary
Calgary, Canada|Calgary, Canada
Introduction:
Spontaneous fluctuations in neural signals had originally been dismissed as noise in resting-state fMRI (rsfMRI) signals. However, it is now known that rsfMRI activity is organized into functionally relevant spatiotemporal patterns (Uddin, 2020), mainly driven by brief high-amplitude events (Zamani Esfahlani et al., 2020). Nevertheless, as rsfMRI is an indirect method, it is unclear to which extent these events reflect spontaneous neuronal activity. This study uses simultaneous intracranial EEG (iEEG)-fMRI to investigate whether spontaneous hemodynamic events in rsfMRI correspond to localized neuronal events in iEEG.
Methods:
We analyzed simultaneous rsfMRI and iEEG data from 29 drug-resistant focal epilepsy patients who required iEEG for presurgical evaluation. fMRI protocol for three to eight 20-minute scans was as follows: echo planar imaging: TE = 30 ms, TR = 1500 ms, flip angle = 65°, 24 cm field of view, 64×64 matrix, 24 to 29 slices, 3.75×3.75× 5.00 mm, 800 volumes. Frames with >1.5 mm motion were excluded, leaving 87 segments of ≥5 minutes to be analyzed. Preprocessing followed (Wilson et al., 2024). For iEEG event detection, spectrograms (1.5-second windows, 75% overlap) were generated for each channel and summed across traditional EEG frequency bands (delta: 0-4 Hz, theta: 4-8 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: 30-100 Hz). For fMRI, the average BOLD signal was calculated within regions of 3×3×3 voxels around each electrode.
Spontaneous events were defined as local peaks exceeding one standard deviation within 6-second windows in both fMRI data and EEG spectrograms. Linear regressions were used to relate the rates of spontaneous events in rsfMRI with the corresponding rates in each iEEG frequency band across iEEG channels. Additionally, to avoid the potential confounding effect of interictal epileptic discharges (IEDs), the analysis was repeated separately for channels with and without IEDs.
Spontaneous events were defined as local peaks exceeding one standard deviation within 6-second windows in both fMRI data and EEG spectrograms. Linear regressions were used to relate the rates of spontaneous events in rsfMRI with the corresponding rates in each iEEG frequency band across iEEG channels. Additionally, to avoid the potential confounding effect of interictal epileptic discharges (IEDs), the analysis was repeated separately for channels with and without IEDs.
Results:
When all data from all segments were pooled together, the delta and beta bands showed significant positive relationships with fMRI in all-channel and non-IED channel analyses (Table 1). In IED channels, however, correlations became negative in all frequency bands, reaching statistical significance for the theta (c = -0.090, p = 0.008) and gamma (c = -0.065, p = 0.035) bands.
At the individual level, analyses were only performed for all channels and non-IED channels, as most subjects only had a small number of IED channels. As shown in Table 2, both positive and negative significant correlations between event rates in iEEG and rsfMRI were observed across all frequency bands. However, more segments showed significant negative correlations in the delta, theta, and alpha bands, while more runs had significant positive correlations in the beta and gamma bands.
Finally, we conducted a group-level analysis (Table 2) to determine whether the observed effects were consistent across runs. The results were statistically significant in the beta band for the all-channel (p=0.021) and non-IED-channel (p=0.037) analyses and showed a trend towards a negative relationship in the delta band for the all-channel (p=0.136) and non-IED-channel (p=0.188) analyses.
To assess motion effects, we correlated event counts with maximum angular and linear displacement from fMRI motion correction. No significant relationships were found, indicating that motion did not affect our findings.
At the individual level, analyses were only performed for all channels and non-IED channels, as most subjects only had a small number of IED channels. As shown in Table 2, both positive and negative significant correlations between event rates in iEEG and rsfMRI were observed across all frequency bands. However, more segments showed significant negative correlations in the delta, theta, and alpha bands, while more runs had significant positive correlations in the beta and gamma bands.
Finally, we conducted a group-level analysis (Table 2) to determine whether the observed effects were consistent across runs. The results were statistically significant in the beta band for the all-channel (p=0.021) and non-IED-channel (p=0.037) analyses and showed a trend towards a negative relationship in the delta band for the all-channel (p=0.136) and non-IED-channel (p=0.188) analyses.
To assess motion effects, we correlated event counts with maximum angular and linear displacement from fMRI motion correction. No significant relationships were found, indicating that motion did not affect our findings.
Conclusions:
This study enhances our understanding of spontaneous fMRI-detected events during resting state and examines fMRI's ability to reflect true neuronal activity. Our findings suggest that fMRI events reflect power increases in the beta band. However, in IED channels, the correlations between fMRI and iEEG events became negative. It is known that large numbers of IEDs may result in reduced amplitudes of fMRI fluctuations (Jacobs et al., 2008), and high IED rates are common in iEEG. Future analyses will explore how these relationships differ during and between IED occurrences.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis 1
Keywords:
Other - Spontaneous neural events, Resting-state fMRI, Intracranial EEG, BOLD
1|2Indicates the priority used for review
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Provide references using APA citation style.
Uddin, L. Q. (2020). Bring the noise: Reconceptualizing spontaneous neural activity. Trends in Cognitive Sciences, 24(9), 734–746.
Wilson, W., Pittman, D. J., Dykens, P., Mosher, V., Gill, L., Peedicail, J., George, A. G., Beers, C. A., Goodyear, B., LeVan, P., Federico, P., & the Calgary Comprehensive Epilepsy Program collaborators. (2024). The hemodynamic response to co‐occurring interictal epileptiform discharges and high‐frequency oscillations localizes the seizure‐onset zone. Epilepsia, 65(9), 2764–2776.
Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2020). High-amplitude cofluctuations in cortical activity drive functional connectivity. Proceedings of the National Academy of Sciences, 117(45), 28393–28401.