Introduction: Using microelectrodes for recording purposes in deep brain stimulation (DBS) has proven to be very useful. Their efficiency can be improved by characterising the properties of extracellular action potentials (EAPs).
Patients And Methods: We analysed the records of nine patients who underwent surgery for epilepsy or aggressiveness under general anaesthesia. The properties of the EAPs of the centromedian, ventral intermediate, ventrocaudal and posteromedial hypothalamic nuclei of the thalamus have been determined.
Results: We have analysed 706 thalamic and 142 hypothalamic cells. The proportion of cell types was found to be specific to each cell nucleus. The most frequent cell type was P1P2N1 (59.5%), followed by N1P1N2 (23.1%). The first phase of the EAP is highly variable. The properties of the EAP phases of the same morphology differ greatly from one nucleus to another.
Conclusions: We have shown that several deep brain nuclei have properties that are specific to the morphology of the EAPs. This will allow for improved localisation of these nuclei during DBS.
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http://dx.doi.org/10.33588/rn.7512.2022187 | DOI Listing |
Ann Clin Transl Neurol
January 2025
NEUROFARBA Department, Neurosciences Section, University of Florence, Florence, Italy.
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Sensors (Basel)
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Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
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Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
View Article and Find Full Text PDFJ Clin Med
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Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
View Article and Find Full Text PDFInt J Mol Sci
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Clinic of Psychiatry, Department of Psychiatry, Medical Department, Wrocław Medical University, 50-367 Wrocław, Poland.
Endometriosis is a widely spread disease that affects about 8% of the world's female population. This condition may be described as a spread of endometrial tissue apart from the uterine cavity, but this process's pathomechanism is still unsure. Apart from classic endometriosis symptoms, which are pelvic pain, infertility, and bleeding problems, there are neuropsychiatric comorbidities that are usually difficult to diagnose.
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