Volumetric and structural connectivity abnormalities co-localise in TLE

Horsley, Jonathan J.; Schroeder, Gabrielle M; Thomas, Rhys; Tisi, Jane de; Vos, Sjoerd B.; Winston, Gavin P.; Duncan, John S.; Wang, Yujiang; Taylor, Peter N

doi:10.1016/j.nicl.2022.103105

Cited by 6 publications

(11 citation statements)

References 84 publications

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“…In machine learning analysis, we report the 95% CI of the AUC using a bias-corrected and accelerated percentile method from 10,000 bootstrap resamples with replacement. 10,31 Data Availability All anonymized brain networks and electrode coordinates with resection indicators included in this study are available at https://doi.org/10.5281/zenodo.8246573.…”

Section: Discussionmentioning

confidence: 99%

“…11 We ran the FreeSurfer “recon-all” pipeline on preoperative T1-weighted MRI to generate gray and white matter surfaces. 10,31…”

Section: Methodsmentioning

confidence: 99%

“…In machine learning analysis, we report the 95% CI of the AUC using a bias-corrected and accelerated percentile method from 10,000 bootstrap resamples with replacement. 10,31…”

Section: Methodsmentioning

confidence: 99%

“…11 We ran the FreeSurfer "recon-all" pipeline on preoperative T1-weighted MRI to generate gray and white matter surfaces. 10,31 Diffusion-weighted MRI was corrected for signal drift, eddy current, and movement artifacts using the FSL eddy_correct tool, and the b vectors were rotated using the FSL fdt-rotatebvecs tool. 10,31 We applied generalized q-sampling imaging reconstruction in DSI studio with a diffusion sampling length ratio of 1.25, followed by deterministic tractography.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…10,31 Diffusion-weighted MRI was corrected for signal drift, eddy current, and movement artifacts using the FSL eddy_correct tool, and the b vectors were rotated using the FSL fdt-rotatebvecs tool. 10,31 We applied generalized q-sampling imaging reconstruction in DSI studio with a diffusion sampling length ratio of 1.25, followed by deterministic tractography. 32 Tractography generated approximately 2 million tracts per person with tracking parameters configured as follows: Runge-Kutta method with step size 1 mm, whole-brain seeding, initial propagation direction set to all fiber orientations, minimum tract length 15 mm, maximum tract length 300 mm, and topology-informed pruning applied with 1 iteration to remove false connections.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

Sinha,

Duncan,

Diehl

et al. 2023

Neurology

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Background and objectives: Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain’s structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship impacts the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control following surgery. Method: We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using pre-surgery functional data from intracranial EEG (iEEG) recordings, pre- and post-surgery structural data from T1-weighted MRI, and pre-surgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using Spearman rank correlation and analyzed this structure-function coupling at two spatial scales: (i) global iEEG network level and (ii) individual iEEG electrode contacts using “virtual surgeries”. We retrospectively predicted post-operative seizure freedom, by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. Result: We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free subjects compared to those with seizure recurrence (p=0.002, d=0.76, AUC=0.78 [95%CI 0.62, 0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p=0.007, d=0.96, AUC=0.73 [95%CI 0.58, 0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 [95%CI 0.67, 0.94]. These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 [95%CI 0.82, 1.0], accuracy of 92%, sensitivity of 93%, and specificity of 91%. Conclusion: Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into pre-surgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. Classification of Evidence: This is a Class IV retrospective case series showing that structure-function mapping may help determine outcome from surgical resection for treatment-resistant focal epilepsy.

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Section: Discussionmentioning

confidence: 99%

“…11 We ran the FreeSurfer “recon-all” pipeline on preoperative T1-weighted MRI to generate gray and white matter surfaces. 10,31…”

Section: Methodsmentioning

confidence: 99%

“…In machine learning analysis, we report the 95% CI of the AUC using a bias-corrected and accelerated percentile method from 10,000 bootstrap resamples with replacement. 10,31…”

Section: Methodsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

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Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

Sinha,

Duncan,

Diehl

et al. 2023

Neurology

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The association between structural connectivity and anti‐seizure medication response in patients with temporal lobe epilepsy

Lee,

Ko,

Kim

et al. 2024

Epilepsia Open

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ObjectivesThis study aimed to investigate the differences in structural connectivity and glymphatic system function between patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) and healthy controls. Additionally, we analyzed the association between structural connectivity, glymphatic system function, and antiseizure medication (ASM) response.MethodsWe retrospectively enrolled patients with TLE and HS and healthy controls who underwent diffusion tensor imaging at our hospital. We assessed structural connectivity in patients with TLE and HS and healthy controls by calculating network measures using graph theory and evaluated glymphatic system function using the diffusion tensor image analysis along the perivascular space (DTI‐ALPS) index. Patients with TLE and HS were categorized into two groups: ASM poor and good responders.ResultsWe enrolled 55 patients with TLE and HS and 53 healthy controls. Of the 55 patients with TLE and HS, 39 were ASM poor responders, and 16 were ASM good responders. The assortativity coefficient in patients with TLE and HS was higher than that in healthy controls (0.004 vs. −0.007, p = 0.004), and the assortativity coefficient in ASM poor responders was lower than that in ASM good responders (−0.001 vs. −0.197, p = 0.003). The DTI‐ALPS index in patients with TLE and HS was lower than that in healthy controls (1.403 vs. 1.709, p < 0.001); however, the DTI‐ALPS index did not differ between ASM poor and good responders (1.411 vs. 1.385, p = 0.628). The DTI‐ALPS index had a significant negative correlation with age in patients with TLE and HS (r = −0.267, p = 0.049).SignificanceWe confirmed increased assortativity coefficient in structural connectivity and decreased DTI‐ALPS index in patients with TLE and HS compared with healthy controls. Additionally, we demonstrated an association between decreased assortativity coefficient in structural connectivity and ASM poor response in patients with TLE patients and HS.Plain Language SummaryThis study investigates the relationship between brain connectivity changes and glymphatic system function with antiseizure medication response in patients with temporal lobe epilepsy and hippocampal sclerosis. The research reveals that these patients show altered brain connectivity and glymphatic function compared to healthy individuals. A key finding is the strong link between a specific connectivity measure (assortativity coefficient) and antiseizure medication response, providing valuable insights that could influence epilepsy treatment and future research directions.

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Complementary structural and functional abnormalities to localise epileptogenic tissue

Horsley,

Thomas,

Chowdhury

et al. 2023

eBioMedicine

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Volumetric and structural connectivity abnormalities co-localise in TLE

Cited by 6 publications

References 84 publications

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

The association between structural connectivity and anti‐seizure medication response in patients with temporal lobe epilepsy

Complementary structural and functional abnormalities to localise epileptogenic tissue

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