Abstract：
Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delineating this entity.
This systematic review aims to analyze the effects of corpus callosum injuries on seizure semiology, providing insights into the neuroscientific and clinical implications of such injuries.
Adhering to the PRISMA guidelines, a comprehensive search across multiple databases, including PubMed/Medline, NIH, Embase, Cochrane Library, and Cross-ref, was conducted until September 25, 2023. Studies on seizures associated with corpus callosum injuries, excluding other cortical or sub-cortical involvements, were included. Machine learning (Random Forest) and deep learning (1D-CNN) algorithms were employed for data classification.
Initially, 1250 articles were identified from the mentioned databases, and additional 350 were found through other relevant sources. Out of all these articles, 41 studies met the inclusion criteria, collectively encompassing 56 patients The most frequent clinical manifestations included generalized tonic-clonic seizures, complex partial seizures, and focal seizures. The most common callosal injuries were related to reversible splenial lesion syndrome and cytotoxic lesions. Machine learning and deep learning analyses revealed significant correlations between seizure types, semiological parameters, and callosal injury locations. Complete recovery was reported in the majority of patients post-treatment.
Corpus callosum injuries have diverse impacts on seizure semiology. This review highlights the importance of understanding the role of the corpus callosum in seizure propagation and manifestation. The findings emphasize the need for targeted diagnostic and therapeutic strategies in managing seizures associated with callosal injuries. Future research should focus on expanding the data pool and exploring the underlying mechanisms in greater detail.
This systematic review aims to analyze the effects of corpus callosum injuries on seizure semiology, providing insights into the neuroscientific and clinical implications of such injuries.
Adhering to the PRISMA guidelines, a comprehensive search across multiple databases, including PubMed/Medline, NIH, Embase, Cochrane Library, and Cross-ref, was conducted until September 25, 2023. Studies on seizures associated with corpus callosum injuries, excluding other cortical or sub-cortical involvements, were included. Machine learning (Random Forest) and deep learning (1D-CNN) algorithms were employed for data classification.
Initially, 1250 articles were identified from the mentioned databases, and additional 350 were found through other relevant sources. Out of all these articles, 41 studies met the inclusion criteria, collectively encompassing 56 patients The most frequent clinical manifestations included generalized tonic-clonic seizures, complex partial seizures, and focal seizures. The most common callosal injuries were related to reversible splenial lesion syndrome and cytotoxic lesions. Machine learning and deep learning analyses revealed significant correlations between seizure types, semiological parameters, and callosal injury locations. Complete recovery was reported in the majority of patients post-treatment.
Corpus callosum injuries have diverse impacts on seizure semiology. This review highlights the importance of understanding the role of the corpus callosum in seizure propagation and manifestation. The findings emphasize the need for targeted diagnostic and therapeutic strategies in managing seizures associated with callosal injuries. Future research should focus on expanding the data pool and exploring the underlying mechanisms in greater detail.
摘要：
背景：经常发现癫痫发作与call体损伤有关，但在大多数情况下,他们仍未被诊断。了解临床，心电图,和神经放射学的变化可以是至关重要的，以界定这个实体。
目的：本系统综述旨在分析call体损伤对癫痫发作的影响，提供对这种损伤的神经科学和临床意义的见解。
方法：遵守PRISMA指南，跨多个数据库的全面搜索，包括PubMed/Medline,NIH,Embase,科克伦图书馆,和交叉引用，一直持续到2023年9月25日。与call体损伤相关的癫痫发作的研究，排除其他皮质或皮质下的参与，包括在内。机器学习（随机森林）和深度学习（1D-CNN）算法用于数据分类。
结果：最初，从上述数据库中确定了1250篇文章，通过其他相关来源发现了另外350个。在所有这些文章中，41项研究符合纳入标准，总共包括56名患者，最常见的临床表现包括全身性强直阵挛性癫痫发作，复杂的部分性癫痫发作,和局灶性癫痫发作。最常见的call骨损伤与可逆性脾病变综合征和细胞毒性病变有关。机器学习和深度学习分析揭示了癫痫发作类型之间的显著相关性，符号学参数，和call骨损伤位置。据报道，大多数患者在治疗后完全康复。
结论：胼胝体损伤对癫痫发作符号学有不同的影响。这篇综述强调了理解call体在癫痫发作传播和表现中的作用的重要性。研究结果强调需要有针对性的诊断和治疗策略来管理与call骨损伤相关的癫痫发作。未来的研究应该集中在扩大数据池和更详细地探索潜在的机制上。
目的：本系统综述旨在分析call体损伤对癫痫发作的影响，提供对这种损伤的神经科学和临床意义的见解。
方法：遵守PRISMA指南，跨多个数据库的全面搜索，包括PubMed/Medline,NIH,Embase,科克伦图书馆,和交叉引用，一直持续到2023年9月25日。与call体损伤相关的癫痫发作的研究，排除其他皮质或皮质下的参与，包括在内。机器学习（随机森林）和深度学习（1D-CNN）算法用于数据分类。
结果：最初，从上述数据库中确定了1250篇文章，通过其他相关来源发现了另外350个。在所有这些文章中，41项研究符合纳入标准，总共包括56名患者，最常见的临床表现包括全身性强直阵挛性癫痫发作，复杂的部分性癫痫发作,和局灶性癫痫发作。最常见的call骨损伤与可逆性脾病变综合征和细胞毒性病变有关。机器学习和深度学习分析揭示了癫痫发作类型之间的显著相关性，符号学参数，和call骨损伤位置。据报道，大多数患者在治疗后完全康复。
结论：胼胝体损伤对癫痫发作符号学有不同的影响。这篇综述强调了理解call体在癫痫发作传播和表现中的作用的重要性。研究结果强调需要有针对性的诊断和治疗策略来管理与call骨损伤相关的癫痫发作。未来的研究应该集中在扩大数据池和更详细地探索潜在的机制上。
