Subcortical functional connectivity gradients in temporal lobe epilepsy

Lucas, Alfredo; Mouchtaris, Sofia; Cornblath, Eli J.; Sinha, Nishant; Caciagli, Lorenzo; Hadar, Peter N.; Gugger, James J; Das, Sandhitsu R.; Stein, Joel M.; Davis, Kenneth A.

doi:10.1016/j.nicl.2023.103418

Cited by 10 publications

(12 citation statements)

References 47 publications

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“…Our findings in L-TLE and R-TLE also support previously published work demonstrating a more widespread network in R-TLE. 3,38,41,42 However, we also add to the literature by demonstrating that L-TLE and R-TLE might have different patterns of thalamic atrophy. Differences in thalamic atrophy dependent on epilepsy laterality have been shown, 2,19 but no specific patterns across thalamic subfields have been described.…”

Section: Discussionmentioning

confidence: 60%

“…This is consistent with reported findings of bilateral hippocampal involvement in R-TLE but not in L-TLE. 3,38,41,42 Absence of Factor 3 in L-TLE was compensated with higher Factor 4 (Cohen d = 1.16, pBON = .070), although the difference was not statistically significant between groups. In L-TLE, we also observed a strong factor cofluctuation between Factor 1 and Factor 4 (Spearman rho = −.75, pBON = .002), suggesting that L-TLE subjects have either ipsilateral hippocampal atrophy, or ipsilateral hippocampal atrophy coupled with bilateral posterior thalamic atrophy.…”

Section: R-tle and L-tle Have Different Distributions Of Thalamic Atr...mentioning

confidence: 83%

“…Structural and functional differences between L-TLE and R-TLE have been described in the literature. 3,38,41,42 Here, we leverage the atrophy factors estimated in our cohort to further characterize hippocampothalamic atrophy pattern differences between L-TLE (n = 18) and R-TLE (n = 8; Figure 4D). We find that there is a significantly higher amount of Factor 3 in R-TLE subjects compared to L-TLE subjects (Cohen d = .88, pBON = .029), suggesting that isolated contralateral hippocampal involvement is present in R-TLE but not L-TLE.…”

Section: R-tle and L-tle Have Different Distributions Of Thalamic Atr...mentioning

confidence: 99%

“…Subcortical structures play a crucial role in the pathogenesis of epilepsy, with both structural 2 and functional 3 changes commonly reported. Therefore, they provide a good opportunity for exploring the use of neuroimaging metrics in epilepsy classification.…”

Section: Introductionmentioning

confidence: 99%

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Mapping hippocampal and thalamic atrophy in epilepsy: A 7‐T magnetic resonance imaging study

Lucas,

Mouchtaris,

Tranquille

et al. 2024

Epilepsia

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ObjectiveEpilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data‐driven alternative for grouping of patients. In this work, we leverage ultra‐high‐field 7‐T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug‐resistant focal epilepsy.MethodsForty‐two drug‐resistant epilepsy patients and 13 controls with 7‐T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data‐driven approach.ResultsIn patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate‐corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L‐TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R‐TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R‐TLE, whereas an anterior thalamic atrophy pattern was more common in L‐TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties.SignificanceLeveraging 7‐T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

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Section: Discussionmentioning

confidence: 60%

Section: R-tle and L-tle Have Different Distributions Of Thalamic Atr...mentioning

confidence: 83%

Section: R-tle and L-tle Have Different Distributions Of Thalamic Atr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping hippocampal and thalamic atrophy in epilepsy: A 7‐T magnetic resonance imaging study

Lucas,

Mouchtaris,

Tranquille

et al. 2024

Epilepsia

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“…Multimodal studies that combine iEEG with neuroimaging, such as diffusion tensor imaging, are also of interest across disciplines, including epilepsy. 11,34,35 Functional neuroimaging, such as resting-state functional MRI, has also demonstrated widespread abnormalities in focal epilepsy, [36][37][38] yet direct intracranial electrophysiological correlates of these abnormalities are lacking. iEEG-recon provides a natural framework for bridging intracranial electrophysiology and neuroimaging by allowing different structural and functional neuroimaging-based atlases to be used in the electrode reconstruction process.…”

Section: Discussionmentioning

confidence: 99%

iEEG‐recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

Lucas,

Scheid,

Pattnaik

et al. 2024

Epilepsia

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ObjectiveClinicians use intracranial EEG (iEEG) in conjunction with non‐invasive brain imaging to identify epileptic networks and target therapy for drug‐resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of “electrode reconstruction,” which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.MethodsWe created iEEG‐recon, a scalable electrode reconstruction pipeline for semi‐automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG‐recon in a containerized format that allows integration into clinical workflows. We propose a cloud‐based implementation of iEEG‐recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.ResultsWe used iEEG‐recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 30‐minute running time per case (including semi‐automatic electrode labeling and reconstruction). iEEG‐recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre‐ and post‐implant T1‐MRI visual inspections. We also found that our use of ANTsPyNet deep learning‐based brain segmentation for electrode classification was consistent with the widely used Freesurfer segmentations.SignificanceiEEG‐recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis, and integration into clinical workflows. iEEG‐recon's accuracy, speed, and compatibility with cloud platforms makes it a useful resource for epilepsy centers worldwide.

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Functional gradients reveal cortical hierarchy changes in multiple sclerosis

De Rosa,

d'Ambrosio,

Bisecco

et al. 2024

Human Brain Mapping

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Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting‐state functional MRI (rs‐fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs‐fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual‐to‐sensorimotor) and a secondary (sensory‐to‐transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory‐transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (, p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network‐level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold‐out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs‐fMRI studies across different MS populations.

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Subcortical functional connectivity gradients in temporal lobe epilepsy

Cited by 10 publications

References 47 publications

Mapping hippocampal and thalamic atrophy in epilepsy: A 7‐T magnetic resonance imaging study

Mapping hippocampal and thalamic atrophy in epilepsy: A 7‐T magnetic resonance imaging study

iEEG‐recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

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