Background/objectives: In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients.
Methods: By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree.
Results: The results obtained from the proposed algorithm provide 100% overlap with the physician's finding on the EEG data.
Conclusions: MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning.
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http://dx.doi.org/10.3390/diagnostics14222509 | DOI Listing |
Seizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
CNS Neurosci Ther
January 2025
Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China.
Background: Adenosine deaminase action on RNA 1 (ADAR1) can convert the adenosine in double-stranded RNA (dsRNA) molecules into inosine in a process known as A-to-I RNA editing. ADAR1 regulates gene expression output by interacting with RNA and other proteins; plays important roles in development, including growth; and is linked to innate immunity, tumors, and central nervous system (CNS) diseases.
Results: In recent years, the role of ADAR1 in tumors has been widely discussed, but its role in CNS diseases has not been reviewed.
J Neurosurg Pediatr
January 2025
2Neurology, UT Southwestern, Dallas, Texas.
Objective: Patients with drug-resistant epilepsy (DRE) are often referred for phase II evaluation with stereo-electroencephalography (SEEG) to identify a seizure onset zone for guiding definitive treatment. For patients without a focal seizure onset zone, neuromodulation targeting the thalamic nuclei-specifically the centromedian nucleus, anterior nucleus of the thalamus, and pulvinar nucleus-may be considered. Currently, thalamic nuclei selection is based mainly on the location of seizure onset, without a detailed evaluation of their network involvement.
View Article and Find Full Text PDFACS Nano
January 2025
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia.
Implantable systems with chronic stability, high sensing performance, and extensive spatial-temporal resolution are a growing focus for monitoring and treating several diseases such as epilepsy, Parkinson's disease, chronic pain, and cardiac arrhythmias. These systems demand exceptional bendability, scalable size, durable electrode materials, and well-encapsulated metal interconnects. However, existing chronic implantable bioelectronic systems largely rely on materials prone to corrosion in biofluids, such as silicon nanomembranes or metals.
View Article and Find Full Text PDFeNeuro
January 2025
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data.
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