Situating the thalamus into large-scale functional network pathology in temporal lobe epilepsy
Poster No:
344
Submission Type:
Abstract Submission
Authors:
Rui Ding1, Ke Xie1, Alexander Ngo1, Judy Chen1, Jordan DeKraker1, Ella Sahlas1, Raúl Rodríguez-Cruces1, Jessica Royer1, Guan Zhou1, Zhiqiang Zhang2, Luis Concha3, Paule Toussaint1, Neda Bernasconi1, Andrea Bernasconi1, Alan Evans1, Boris Bernhardt1
Institutions:
1McGill University, Montreal, Quebec, 2Nanjing University School of Medicine, Nanjing, Jiangsu, 3Universidad Nacional Autónoma de Mexico, Mexico, Queretaro
First Author:
Co-Author(s):
Introduction:
The thalamus is a subcortical region which is thought to play a key role in mediating large-scale network function (Shine et al., 2023). In addition to its role in healthy brain organization, the thalamus has been implicated in multiple epilepsy syndromes (Caciagli et al., 2020). In temporal lobe epilepsy (TLE), the most common pharmacoresistant epilepsy in adults, the thalamus is thought to be a core node in a pathophysiological network between the hippocampal disease epicenter and broad neocortical systems (Dinkelacker et al., 2015; Keller et al., 2014). Here, we leveraged resting-state fMRI to explore network alterations in hippocampal-thalamo-cortical networks in TLE patients and healthy controls across several imaging sites.
Methods:
We analyzed 3T resting-state fMRI (Rs-fMRI) data from 103 unilateral TLE patients and 123 healthy controls (HC) across three independent sites:MICA-MICs (Royer et al., 2022), NKG , and EpiC (Rodríguez-Cruces et al., 2020). Thalamic parcellation was performed using a Rs-fMRI gradient-based subcortical atlas, which includes eight nuclei(Tian et al., 2020). Data were processed using micapipe (Cruces et al., 2022), a comprehensive toolbox for MRI preprocessing, data integration, and connectome analysis. Preprocessed fMRI timeseries were first registered to native FreeSurfer space and resampled to the Conte69 space (Van Essen et al., 2012), followed by 10 mm full-width at half maximum(FWHM) smoothing to generate the cortical timeseries. The hippocampus was segmented automatically using HippUnfold(DeKraker et al., 2022). fMRI timeseries were mapped onto the hippocampal midthickness surface and smoothed (3 mm FWHM). The thalamus mask was segmented using FreeSurfer, registered to MNI 152 2mm space, and refined with thalamic subfields. Volumetric fMRI timeseries were aligned to the same space to extract whole-thalamus and thalamic nucleus timeseries. Rs-fMRI timeseries were used to compute the average timeseries of the global thalamus and individual thalamic nuclei. Pearson correlations were calculated between these timeseries and the vertex-wise timeseries of the neocortex and hippocampus (Fig.1A). We statistically compared connectivity patterns between TLE and HC, both at the level of the global thalamus and at the level of thalamic subfields. To identify the subfields most affected in thalamo-cortical and thalamo-hippocampal networks, we performed dominance analysis.
Results:
TLE patients presented with marked reductions in global thalamo-cortical and thalamo-hippocampal connectivity. Neocortical connectivity reductions were primarily observed in the prefrontal, precentral, postcentral, and visual cortices, while hippocampal connectivity reductions were most prominent in the CA1, CA4, and subiculum regions (Fig.1C). Results were consistent across all three sites analyzed individual (Fig.1D). Analysis of thalamic subnuclei revealed generally a bilateral reduction in connectivity to cortical systems, whereas hippocampal connectivity was predominantly impacted ipsilaterally (Fig.2A,B). Functional connectivity alterations thalamus and cortex was primarily driven by the lateral ventroposterior divisions (VPL), while thalamo-hippocampal connectivity was most closely related to medial dorsoanterior (DAm) connectivity (Fig.2C).
Conclusions:
Our multiscale functional findings reveal disruptions hippocampal-thalamo-cortical functional networks in TLE by integrating multiscale functional analyses across three independent datasets. The ipsilateral hippocampal-thalamo alterations mainly indicate TLE mainly affected hippocampus epicenter and thalamus while the bilateral thalamo-cortical alterations suggest a broader influence extending to the entire brain. We identified thalamic subnuclei contributing to these alterations, underscoring the thalamus as a central hub in TLE pathophysiology. This highlights the need for further studies on cognitive and clinical associations and cross-cohort analyses to confirm specificity.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Epilepsy
FUNCTIONAL MRI
Sub-Cortical
Thalamus
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cruces, R. R. (2022). Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 263, 119612.
DeKraker, J. (2022). Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. eLife, 11, e77945.
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Keller, S. S. (2014). Thalamotemporal impairment in temporal lobe epilepsy: A combined MRI analysis of structure, integrity, and connectivity. Epilepsia, 55(2), 306-315.
Rodríguez-Cruces, R. (2020). Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. NeuroImage, 213, 116706.
Royer, J.(2022). An Open MRI Dataset For Multiscale Neuroscience. Scientific Data, 9(1), 569.
Shine, J. M.(2023). The impact of the human thalamus on brain-wide information processing. Nat Rev Neurosci, 24(7), 416-430.
Tian, Y. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature Neuroscience, 23(11), 1421-1432.
Van Essen, D. C. (2012). Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cerebral cortex, 22(10), 2241-2262.