{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:10:58Z","timestamp":1789132258717,"version":"build-2803163510"},"reference-count":134,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:p>\n                    <jats:italic>In vivo<\/jats:italic>\n                    calcium imaging is a powerful technique for the monitoring activity of large neuronal populations in the intact brain. Two-photon microscopy provides subcellular resolution in head-fixed animals, and miniaturized fluorescence microscopy (miniscope) enables recordings of neuronal network activity in freely behaving animals. Combined with genetically encoded calcium indicators, these methods have revealed important new information about the functioning of neuronal circuits and enabled investigation of neuronal network dysfunction in mouse models of Alzheimer\u2019s disease. Typically, such imaging data are analyzed using event-based and statistical approaches, which have been highly informative but fail to capture the higher-order spatiotemporal structure of circuit activity. Here we propose that dimensionality reduction and neuronal manifold analysis, machine learning, and artificial intelligence (AI)-based methods provide powerful data-driven approaches, enabling feature extraction, detection of disease-associated network states, and simultaneous analysis of neural network activity and behavior. Crucially, beyond describing pathology, these approaches could offer a more sensitive and complementary readout for screening candidate therapeutic strategies in neurological disease models. Here, we summarize neuronal activity alterations identified across Alzheimer\u2019s disease mouse models using classical and AI-based calcium imaging analyses, and outline future perspectives for AI-driven approaches in this field, from interpretable architectures to multimodal integration of activity and behavior.\n                  <\/jats:p>","DOI":"10.3389\/fnagi.2026.1930851","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T12:48:35Z","timestamp":1789130915000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dissecting neuronal circuit function and dysfunction in Alzheimer\u2019s disease mouse models: from conventional calcium imaging analysis to machine learning-based approaches"],"prefix":"10.3389","volume":"18","author":[{"given":"Evgenii","family":"Gerasimov","sequence":"first","affiliation":[{"name":"Laboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University","place":["Saint Petersburg, Russia"]},{"name":"Laboratory of Molecular Neurobiology, Pavlov Institute of Physiology, Russian Academy of Sciences","place":["Saint Petersburg, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgenii","family":"Fedorov","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Imaging and Data Analysis, Peter the Great St. Petersburg Polytechnic University","place":["Saint Petersburg, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilya","family":"Bezprozvanny","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University","place":["Saint Petersburg, Russia"]},{"name":"Laboratory of Molecular Neurobiology, Pavlov Institute of Physiology, Russian Academy of Sciences","place":["Saint Petersburg, Russia"]},{"name":"School of Basic Medical Sciences, Inner Mongolia Medical University, Hohhot","place":["Inner Mongolia, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaterina","family":"Pchitskaya","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular Neurodegeneration, Peter the Great St. Petersburg Polytechnic University","place":["Saint Petersburg, Russia"]},{"name":"Laboratory of Biomedical Imaging and Data Analysis, Peter the Great St. Petersburg Polytechnic University","place":["Saint Petersburg, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1849742","DOI":"10.3389\/fpsyg.2026.1849742","article-title":"Bridging the neuro-AI chasm: A framework for scalable, contextually adaptive training resources in large-scale brain data science.","volume":"17","author":"Abrams","year":"2026","journal-title":"Front Psychol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncel.2019.00141","article-title":"Circuit investigations with open-source miniaturized microscopes: Past, present and future.","volume":"13","author":"Aharoni","year":"2019","journal-title":"Front. 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