{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:05:51Z","timestamp":1792109151426,"version":"4.4.0"},"reference-count":86,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"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. Neurosci."],"abstract":"<jats:p>Intracortical recordings can be used to voluntarily control external devices <jats:italic>via<\/jats:italic> brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2\/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.<\/jats:p>","DOI":"10.3389\/fnins.2023.1096097","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T05:22:34Z","timestamp":1680672154000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Layer-dependent stability of intracortical recordings and neuronal cell loss"],"prefix":"10.3389","volume":"17","author":[{"given":"Morgan E.","family":"Urdaneta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas G.","family":"Kunigk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesus D.","family":"Pe\u00f1aloza-Aponte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seth","family":"Currlin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian G.","family":"Malone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shelley I.","family":"Fried","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin J.","family":"Otto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","article-title":"Trainable Weka segmentation: a machine learning tool for microscopy pixel classification","volume":"33","author":"Arganda-Carreras","year":"2017","journal-title":"Bioinformatics"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"026003","DOI":"10.1088\/1741-2560\/13\/2\/026003.Scanning","article-title":"Intracortical microelectrode arrays in non-human primates","volume":"13","author":"Barrese","year":"2016","journal-title":"J. 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