Advances in Skin, Wound and Tissue Repair

en AI-Driven Optimization of Long-Term Potentiation Protocols in an Experimental Model of Alzheimer’s Disease AI-Driven Optimization of Long-Term Potentiation Protocols in an Experimental Model of Alzheimer’s Disease عمومى General پژوهشي Research Background: Alzheimer’s disease (AD), one of the most prevalent neurodegenerative disorders, is marked by progressive memory loss and cognitive decline. With the rapid advancement of artificial intelligence (AI), data-driven approaches have become increasingly important in both basic and clinical neuroscience research. While animal models remain indispensable for elucidating disease mechanisms and testing therapeutic strategies, such experiments are often costly and time-consuming. This study, therefore, sought to refine the experimental protocol for inducing long-term potentiation (LTP) in an animal model of AD through AI-driven data mining techniques. Methods: Electrophysiological data were obtained from male Wistar rats (n = 36) divided into three groups: control, sham-operated, and AD. The AD model was induced via selective lesioning of the Basal Nucleus of Meynert (NBM) using ibotenic acid. Data preprocessing and analysis were performed in Python. Feature selection and ranking were conducted using Mutual Information, Gain Ratio, and Gini Index, followed by correlation-based filtering through the visualization of heatmaps. Features with low predictive power or high redundancy were systematically excluded to construct a more efficient classification framework. Results: Feature importance analysis identified Mean Per.PSA.B6–B8 and Mean Per.Slop.B6–B8 as highly predictive variables, whereas pre-tetanic measures (Mean Per.PSA.B3–B4 and Mean Per.Slop.B3–B4) contributed minimally and were excluded. Likewise, high-intensity stimulation features (Mean PSA600–1000 and Mean Slop600–1000) demonstrated more substantial predictive value compared to low-intensity counterparts (<600 μA), supporting their retention in the optimized protocol. Correlation analysis confirmed these findings by highlighting redundancy among low-importance features. Conclusions: The integration of feature importance metrics with correlation-based filtering enabled the identification of key electrophysiological markers while eliminating redundant variables from the LTP protocol. This optimization enhanced the accuracy, stability, and interpretability of machine learning models, while simultaneously reducing experimental costs, duration, and data collection requirements. Dimensionality reduction further improved computational efficiency and predictive performance, particularly in complex architectures. Significance: This study introduces the first evidence-based, machine learning–guided protocol optimization framework in Alzheimer’s research, bridging computational intelligence with neurophysiological modeling to accelerate translational discovery.   Alzheimer’s Disease, Basal Nucleus of Meynert, Long-term Potentiation (LTP), Artificial Intelligence, Feature Selection, Data Mining, Wistar Rat 42 47 http://icml.ir/browse.php?a_code=A-10-652-2&slc_lang=en&sid=1 Haleh Fateh هاله فاتح hfateh203@gmail.com 1050031947532846005538 1050031947532846005538 No Lifestyle medicine Research Group, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran Mania Asgharpour مانیا اصغرپور Mania.asgharpour@kiau.ac.ir 1050031947532846005539 1050031947532846005539 No Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran Mojtaba Khayatajami مجتبی خیاط عجمی mj.ajami8@gmail.com 1050031947532846005540 1050031947532846005540 No Lifestyle medicine Research Group, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran Seyed Behnamedin Jameie سید بهنام الدین جامعی jameie.sb@iums.ac.ir 1050031947532846005541 1050031947532846005541 No Neurosciencr research center, Iran university of medical sciences, tehran, Iran Arya Derakhshesh آریا درخشش arya.derakhshesh@yahoo.com 1050031947532846005542 1050031947532846005542 No Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran Mona Farhadi مونا فرهادی monafarhadi@yahoo.com 1050031947532846005543 1050031947532846005543 No Department of Microbiology, Ka.C., Islamic Azad University, Karaj, Iran Sobhan Kazemi سبحان کاظمی sobhan.kaz@gmail.com 1050031947532846005544 1050031947532846005544 No School of Medicine, Iran University of Medical Sciences, Tehran, Iran Hesameddin Allameh حسام الدین علامه allameh@doctor.com 1050031947532846005545 1050031947532846005545 No Lifestyle medicine Research Group, Academic Center for Education, Culture and Research (ACECR), Tehran, Iran. Nasrin Hosseini نسرین حسینی hosseini.n58@gmail.com 1050031947532846005546 1050031947532846005546 Yes Neurosciencr research center, Iran university of medical sciences, tehran, Iran