A generalizable model of individual-specific networks in drug-resistant epilepsy
Poster No:
1360
Submission Type:
Abstract Submission
Authors:
Mervyn Lim1,2,3, Shaoshi Zhang2, Shreya Pande2, Aihuiping Xue2, Ru Kong2, Kareem Zaghloul3, Sara Inati3, Thomas Yeo2
Institutions:
1Division of Neurosurgery, National University Hospital, Singapore, Singapore, 2Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore, 3National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD
First Author:
Mervyn Lim
Division of Neurosurgery, National University Hospital|Centre for Sleep and Cognition, National University of Singapore|National Institute of Neurological Disorders and Stroke, National Institute of Health
Singapore, Singapore|Singapore, Singapore|Bethesda, MD
Division of Neurosurgery, National University Hospital|Centre for Sleep and Cognition, National University of Singapore|National Institute of Neurological Disorders and Stroke, National Institute of Health
Singapore, Singapore|Singapore, Singapore|Bethesda, MD
Co-Author(s):
Kareem Zaghloul
National Institute of Neurological Disorders and Stroke, National Institute of Health
Bethesda, MD
National Institute of Neurological Disorders and Stroke, National Institute of Health
Bethesda, MD
Sara Inati
National Institute of Neurological Disorders and Stroke, National Institute of Health
Bethesda, MD
National Institute of Neurological Disorders and Stroke, National Institute of Health
Bethesda, MD
Introduction:
Epilepsy is associated with decreased functional connectivity and altered spatial topography compared to healthy participants (Bettus et al., 2008; Pittau, Grova, Moeller, Dubeau, & Gotman, 2012). However, individual-specific resting-state cortical networks in drug-resistant epilepsy is not yet well characterized (Li et al., 2020). We previously developed a multi-session hierarchical Bayesian model (MS-HBM) that can estimate high-quality individual-specific networks with limited quantity of functional magnetic resonance imaging data (Kong et al., 2019). Here, we applied our methods for deriving population-level (Yeo et al., 2011) and individual-specific (Kong et al., 2019) networks of the cerebral cortex to explore network organization in two independent cohorts of drug-resistant epilepsy.
Methods:
We compared population-level and individual-specific networks estimated using 40 healthy controls and 26 patients with temporal lobe and extra-temporal lobe epilepsy (Thompson et al., 2020). We showed that individual-specific networks using MS-HBM trained on drug-resistant epilepsy better captured the organization of the cerebral cortex during resting-state and intracranial electrical stimulation compared to population-level networks or MS-HBM trained on healthy participants. Next, we show that group-averaged amygdala stimulation overlooks inter-individual differences in topographic activation across individual-specific networks. Finally, we show that the previously trained MS-HBM generalized well to an independent dataset of 21 participants with drug-resistant epilepsy from a different site (Rolinski et al., 2020). We provide a final MS-HBM trained on both datasets as a resource for future studies.
Results:
Drug-resistant epilepsy networks were observed to be more focal and even unilateral compared to healthy controls (Figure 1). Individual-specific networks performed better than population-level networks in resting-state homogeneity (population-level network: 0.153 ± 0.031 vs. individual-specific network: 0.168 ± 0.035; p<0.001; higher is better) and electrical-stimulation inhomogeneity (population-level network: 5.304 ± 0.189 vs. individual-specific network: 5.247 ± 0.212; p<0.001; lower is better).
Generalised linear models showing the z-score activation maps (thresholded and cluster-corrected) of group-averaged amygdala electrical stimulation showed similar functional connectivity to cortical areas identified by previous studies (Kerestes, Chase, Phillips, Ladouceur, & Eickhoff, 2017; Roy et al., 2009; Sawada et al., 2022). Using individual-specific networks, we observed high inter-individual variability and show that group-averaged amygdala stimulation overlooked inter-individual differences in topographic activation across individual-specific networks (Figure 2).
The previously trained MS-HBM generalized well to an independent dataset of 21 participants with drug-resistant epilepsy from a different site (population-level network: 0.197 ± 0.040 vs. individual-specific network: 0.214 ± 0.044; p<0.001; higher is better). We trained a final MS-HBM on both datasets, which estimated individual-networks that had the highest resting-state homogeneity (0.168 ± 0.035) and lowest electrical stimulation inhomogeneity (5.245 ± 0.218) compared to all other networks tested.
Generalised linear models showing the z-score activation maps (thresholded and cluster-corrected) of group-averaged amygdala electrical stimulation showed similar functional connectivity to cortical areas identified by previous studies (Kerestes, Chase, Phillips, Ladouceur, & Eickhoff, 2017; Roy et al., 2009; Sawada et al., 2022). Using individual-specific networks, we observed high inter-individual variability and show that group-averaged amygdala stimulation overlooked inter-individual differences in topographic activation across individual-specific networks (Figure 2).
The previously trained MS-HBM generalized well to an independent dataset of 21 participants with drug-resistant epilepsy from a different site (population-level network: 0.197 ± 0.040 vs. individual-specific network: 0.214 ± 0.044; p<0.001; higher is better). We trained a final MS-HBM on both datasets, which estimated individual-networks that had the highest resting-state homogeneity (0.168 ± 0.035) and lowest electrical stimulation inhomogeneity (5.245 ± 0.218) compared to all other networks tested.
Conclusions:
We provided a method for estimating reliable high-quality individual-specific cortical networks in drug-resistant epilepsy. Individual-specific networks may provide a useful tool to better understand inter-individual differences in spatial topography and cortical activation of intracranial electrical stimulation in the future.
Brain Stimulation:
Invasive Stimulation Methods Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Segmentation and Parcellation
Keywords:
Cortex
Epilepsy
FUNCTIONAL MRI
Other - Individual-specific networks; Neuromodulation; Intracranial electrical stimulation
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kerestes, R., Chase, H. W., Phillips, M. L., Ladouceur, C. D., & Eickhoff, S. B. (2017). Multimodal evaluation of the amygdala's functional connectivity. Neuroimage, 148, 219-229. doi:10.1016/j.neuroimage.2016.12.023
Kong, R., Li, J., Orban, C., Sabuncu, M. R., Liu, H., Schaefer, A., . . . Yeo, B. T. T. (2019). Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb Cortex, 29(6), 2533-2551. doi:10.1093/cercor/bhy123
Li, R., Wang, H., Wang, L., Zhang, L., Zou, T., Wang, X., . . . Chen, H. (2020). Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy. Human Brain Mapping, 42(2), 412-426. doi:10.1002/hbm.25231
Pittau, F., Grova, C., Moeller, F., Dubeau, F., & Gotman, J. (2012). Patterns of altered functional connectivity in mesial temporal lobe epilepsy. Epilepsia, 53(6), 1013-1023. doi:10.1111/j.1528-1167.2012.03464.x
Rolinski, R., You, X., Gonzalez‐Castillo, J., Norato, G., Reynolds, R. C., Inati, S. K., & Theodore, W. H. (2020). Language lateralization from task‐based and resting state functional MRI in patients with epilepsy. Human Brain Mapping, 41(11), 3133-3146. doi:10.1002/hbm.25003
Roy, A. K., Shehzad, Z., Margulies, D. S., Kelly, A. M., Uddin, L. Q., Gotimer, K., . . . Milham, M. P. (2009). Functional connectivity of the human amygdala using resting state fMRI. Neuroimage, 45(2), 614-626. doi:10.1016/j.neuroimage.2008.11.030
Sawada, M., Adolphs, R., Dlouhy, B. J., Jenison, R. L., Rhone, A. E., Kovach, C. K., . . . Oya, H. (2022). Mapping effective connectivity of human amygdala subdivisions with intracranial stimulation. Nat Commun, 13(1), 4909. doi:10.1038/s41467-022-32644-y
Thompson, W. H., Nair, R., Oya, H., Esteban, O., Shine, J. M., Petkov, C. I., . . . Adolphs, R. (2020). A data resource from concurrent intracranial stimulation and functional MRI of the human brain. Sci Data, 7(1), 258. doi:10.1038/s41597-020-00595-y
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., . . . Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol,