Application of combined multimodal neuroimaging and video-electroencephalography in intractable epilepsy patients for improved post-surgical outcome prediction

Yu, Kun; Cheng, Nan; Dang, Na; Xb, Hu; Zhang, G-Q; Dong, Y-W; Wang, X; Jy, Gao

doi:10.1016/j.crad.2021.12.013

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…These mutations disrupt the mTOR (mammalian target of rapamycin) pathway, leading to abnormal cell proliferation and neuronal hyperexcitability. Understanding DEPDC5-related epilepsy has implications for targeted therapies [29].…”

Section: Specific Gene Mutations Associated With Epilepsy Subtypesmentioning

confidence: 99%

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape

Abdelsamad,

Kachhadia,

Hassan

et al. 2023

Cureus

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Epilepsy, a neurological disorder characterized by recurrent seizures, has witnessed a remarkable transformation in its classification paradigm, driven by advances in clinical understanding, neuroimaging, and molecular genetics. This narrative review navigates the dynamic landscape of epilepsy classification, offering insights into recent developments, challenges, and the promising horizon. Historically, epilepsy classification relied heavily on clinical observations, categorizing seizures based on their phenomenology and presumed etiology. However, the field has profoundly shifted from a symptom-based approach to a more refined, multidimensional system. One pivotal aspect of this evolution is the integration of neuroimaging techniques, particularly magnetic resonance imaging (MRI) and functional imaging modalities. These tools have unveiled the intricate neural networks implicated in epilepsy, facilitating the identification of distinct brain abnormalities and the categorization of epilepsy subtypes based on structural and functional findings. Furthermore, the role of genetics has become increasingly prominent in epilepsy classification. Genetic discoveries have not only unraveled the molecular underpinnings of various epileptic syndromes but have also provided valuable diagnostic and prognostic insights. This narrative review delves into the expanding realm of genetic testing and its impact on tailoring treatment strategies to individual patients. As the classification landscape evolves, there are accompanying challenges. The narrative review underscores the transformative potential of artificial intelligence and machine learning in epilepsy classification. These technologies hold promise in automating the analysis of complex neuroimaging and genetic data, offering enhanced accuracy and efficiency in epilepsy diagnosis and classification. In conclusion, navigating the shifting landscape of epilepsy classification is a journey marked by progress, complexity, and the prospect of improved patient care. We are charting a course toward more precise diagnoses and tailored treatments by embracing advanced neuroimaging, genetics, and innovative technologies. As the field continues to evolve, collaborative efforts and a holistic understanding of epilepsy's diverse manifestations will be instrumental in harnessing the full potential of this dynamic landscape.

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Section: Specific Gene Mutations Associated With Epilepsy Subtypesmentioning

confidence: 99%

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape

Abdelsamad,

Kachhadia,

Hassan

et al. 2023

Cureus

View full text Add to dashboard Cite

show abstract

Application value of multimodal MRI combined with PET metabolic parameters in temporal lobe epilepsy with dual pathology

Kong,

Cheng,

Qiu

et al. 2023

European Journal of Radiology

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Application of combined multimodal neuroimaging and video-electroencephalography in intractable epilepsy patients for improved post-surgical outcome prediction

Cited by 2 publications

References 28 publications

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape

Application value of multimodal MRI combined with PET metabolic parameters in temporal lobe epilepsy with dual pathology

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