Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns

Maher, Christina; D’Souza, Arkiev; Barnett, Michael; Kavehei, Omid; Wang, Chenyu; Nikpour, Armin

doi:10.3390/app122010487

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…On a pharmacological note, Pieróg et al investigated the effects of ellagic acid on seizure threshold, revealing its potential as an adjunct therapy in seizure management [54]. Maher et al delved into the correlation between brain structure and function to unravel connectivity patterns preceding seizures, thus providing a blueprint for targeted therapeutic interventions [55]. Finally, the work of Arocha Pérez et al focused on the semiology of seizures and cerebral perfusion patterns in drug-resistant focal epilepsies, contributing to a better understanding of neural network dynamics in epilepsy [56].…”

Section: Discussionmentioning

confidence: 99%

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress

Bekbolatova,

Mayer,

Jose

et al. 2024

Brain Sciences

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Epilepsy is one of the most common neurological disorders globally, affecting about 50 million people, with nearly 80% of those affected residing in low- and middle-income countries. It is characterized by recurrent seizures that result from abnormal electrical brain activity, with seizures varying widely in manifestation. The exploration of the biomechanical effects that seizures have on brain dynamics and stress levels is relevant for the development of more effective treatments and protective strategies. This study uses a blend of experimental data and computational simulations to assess the brain’s physical response during seizures, particularly focusing on the behavior of cerebrospinal fluid and the resulting mechanical stresses on different brain regions. Notable findings show increases in stress, predominantly in the posterior gyri and brainstem, during seizures and an evidence of brain displacement relative to the skull. These observations suggest a dynamic and complex interaction between the brain and skull, with maximum shear stress regions demonstrating the limited yet essential protective role of the CSF. By providing a deeper understanding of the mechanical changes occurring during seizures, this research supports the goal of advancing diagnostic tools, informing more targeted treatment interventions, and guiding the creation of customized therapeutic strategies to enhance neurological care and protect against the adverse effects of seizures.

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Section: Discussionmentioning

confidence: 99%

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress

Bekbolatova,

Mayer,

Jose

et al. 2024

Brain Sciences

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show abstract

“…The copyright holder for this preprint this version posted February 10, 2023. ; https://doi.org/10.1101/2023.02.09.23285681 doi: medRxiv preprint data from electroencephalography (EEG) has revealed the influence of structurefunction coupling in seizure dynamics [15,16], seizure propagation [17], and postoperative seizure freedom [18]. Importantly, phenomenologic models have shown that structural alterations can give rise to FBTCS [19].…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of structural connectomes in focal epilepsy has yielded a range of network-based biomarkers that may aid pre-surgical selection and pharmacological treatment planning [6, 13, 14]. The combination of structural connectomes with functional data from electroencephalography (EEG) has revealed the influence of structure-function coupling in seizure dynamics [15, 16], seizure propagation [17], and postoperative seizure freedom [18]. Importantly, phenomenologic models have shown that structural alterations can give rise to FBTCS [19].…”

Section: Introductionmentioning

confidence: 99%

Deep learning distinguishes focal epilepsy groups using connectomes: Feasibility and clinical implications

Maher

Tang

D’Souza

et al. 2023

Preprint

Self Cite

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The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

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Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications

Maher,

Tang,

D’Souza

et al. 2023

Brain Communications

View full text Add to dashboard Cite

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model’s interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

show abstract

Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns

Cited by 3 publications

References 45 publications

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress

Deep learning distinguishes focal epilepsy groups using connectomes: Feasibility and clinical implications

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications

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