10.18.2024
Functional neuroimaging tools have demonstrated substantial potential to provide quantifiable insight into brain function in various neurologic disorders, particularly AD. Entropy, a novel analysis for better understanding the nonlinear nature of neurophysiological data, has demonstrated consistent accuracy in disease detection. This literature review characterizes the use of entropy-based analyses from functional neuroimaging tools, in patients with AD for disease detection, therapeutic response measurement, and providing clinical insights.
10.25.2023
Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals’ responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD.
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Resting-state brain networks offer potential biomarkers for predicting levodopa response in Parkinson’s disease. By analyzing functional connectivity patterns through neuroimaging, this study identifies specific network alterations associated with motor and non-motor improvements. These findings highlight the value of resting-state connectivity as a non-invasive tool for optimizing individualized treatment strategies.
Algorithmic seizure forecasting technology is advancing rapidly, utilizing machine learning and neurophysiological data to predict epileptic seizures. This review explores current developments, including model accuracy, patient-specific adaptations, and real-world implementation challenges. Improved forecasting promises to enhance patient safety, inform therapeutic interventions, and reduce the burden of epilepsy through predictive care.
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The decentralization of epilepsy clinical trials leverages digital biomarkers and ambulatory seizure detection technologies to enhance trial accessibility and data accuracy. This approach integrates wearable devices, remote monitoring, and AI-driven analytics, enabling real-time seizure detection. Decentralized trials improve patient engagement, reduce costs, and offer more comprehensive insights into epilepsy treatments.
