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A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.
J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFA hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.
BMC Med Inform Decis Mak
January 2025
The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.
Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study.
Migraine and cognitive dysfunction: a narrative review.
J Headache Pain
December 2024
Department of Neurology, Headache Medical Center, Linz, Austria.
The association between migraine and cognitive function has been studied during the last decade, however, this relationship is not well established. As migraine prevalence is highest between the ages of 30-40, aligning with some of our most productive years, we must understand cognitive changes within this disorder. Cognitive impairment potentially limits social and professional interactions, thus negatively impacting quality of life.
View Article and Find Full Text PDFArticle Synopsis
- Longer periods between headache episodes may allow the nervous system to recover better, possibly enhancing patients' quality of life and disease status.
- The study, PROMISE-2, analyzed data from over 1,000 chronic migraine patients to examine the relationship between the length of interictal periods (time between headaches) and various patient-reported health outcomes.
- Results showed that patients with interictal periods longer than 21 days reported significantly better improvements in headache impact, overall health status, and bothersome symptoms compared to those with shorter periods.
Preictal period optimization for deep learning-based epileptic seizure prediction.
J Neural Eng
December 2024
Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom.
. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the incomplete understanding and variability of the preictal state imposes challenges in identifying the optimal preictal period (OPP) for labeling the EEG segments.
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