{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:50:35Z","timestamp":1790833835886,"version":"4.1.0"},"reference-count":77,"publisher":"Society for Neuroscience","issue":"8","license":[{"start":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T00:00:00Z","timestamp":1566259200000},"content-version":"vor","delay-in-days":229,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Neurosci."],"published-print":{"date-parts":[[2019,2,20]]},"abstract":"<jats:p>Neural activity in the premotor and motor cortices shows prominent structure in the beta frequency range (13\u201330 Hz). Currently, the behavioral relevance of this beta band activity (BBA) is debated. The underlying source of motor BBA and how it changes as a function of cortical depth are also not completely understood. Here, we addressed these unresolved questions by investigating BBA recorded using laminar electrodes in the dorsal premotor cortex of 2 male rhesus macaques performing a visual reaction time (RT) reach discrimination task. We observed robust BBA before and after the onset of the visual stimulus but not during the arm movement. While poststimulus BBA was positively correlated with RT throughout the beta frequency range, prestimulus correlation varied by frequency. Low beta frequencies (\u223c12\u201320 Hz) were positively correlated with RT, and high beta frequencies (\u223c22\u201330 Hz) were negatively correlated with RT. Analysis and simulations suggested that these frequency-dependent correlations could emerge due to a shift in the component frequencies of the prestimulus BBA as a function of RT, such that faster RTs are accompanied by greater power in high beta frequencies. We also observed a laminar dependence of BBA, with deeper electrodes demonstrating stronger power in low beta frequencies both prestimulus and poststimulus. The heterogeneous nature of BBA and the changing relationship between BBA and RT in different task epochs may be a sign of the differential network dynamics involved in cue expectation, decision-making, motor preparation, and movement execution.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>SIGNIFICANCE STATEMENT<\/jats:bold>\n                    Beta band activity (BBA) has been implicated in motor tasks, in disease states, and as a potential signal for brain\u2013machine interfaces. However, the behavioral relevance of BBA and its laminar organization in premotor cortex have not been completely elucidated. Here we addressed these unresolved issues using simultaneous recordings from multiple cortical layers of the premotor cortex of monkeys performing a decision-making task. Our key finding is that BBA is not a monolithic signal. Instead, BBA consists of at least two frequency bands. The relationship between BBA and eventual behavior, such as reaction time, also dynamically changes depending on task epoch. We also provide further evidence that BBA is laminarly organized, with greater power in deeper electrodes for low beta frequencies.\n                  <\/jats:p>","DOI":"10.1523\/jneurosci.1066-18.2018","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T17:55:14Z","timestamp":1546538114000},"page":"1420-1435","source":"Crossref","is-referenced-by-count":50,"title":["Frequency Shifts and Depth Dependence of Premotor Beta Band Activity during Perceptual Decision-Making"],"prefix":"10.1523","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1711-590X","authenticated-orcid":false,"given":"Chandramouli","family":"Chandrasekaran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3029-8309","authenticated-orcid":false,"given":"Iliana E.","family":"Bray","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-9240","authenticated-orcid":false,"given":"Krishna V.","family":"Shenoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"2023041803345700000_39.8.1420.1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/5\/1\/003"},{"key":"2023041803345700000_39.8.1420.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2008.01.007"},{"key":"2023041803345700000_39.8.1420.3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-7793.1997.225bo.x"},{"key":"2023041803345700000_39.8.1420.4","doi-asserted-by":"publisher","DOI":"10.1007\/s002210050825"},{"key":"2023041803345700000_39.8.1420.5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1710323115"},{"key":"2023041803345700000_39.8.1420.6","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J R Stat Soc Series B Stat Methodol"},{"key":"2023041803345700000_39.8.1420.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.02.078"},{"key":"2023041803345700000_39.8.1420.8","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4522(87)92965-4"},{"key":"2023041803345700000_39.8.1420.9","doi-asserted-by":"publisher","DOI":"10.1111\/ejn.12574"},{"key":"2023041803345700000_39.8.1420.10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-211-45295-0_6"},{"key":"2023041803345700000_39.8.1420.11","doi-asserted-by":"publisher","DOI":"10.1126\/science.1138071"},{"key":"2023041803345700000_39.8.1420.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.09.029"},{"key":"2023041803345700000_39.8.1420.13","doi-asserted-by":"publisher","DOI":"10.1111\/ejn.12453"},{"key":"2023041803345700000_39.8.1420.14","doi-asserted-by":"publisher","DOI":"10.1152\/jn.91095.2008"},{"key":"2023041803345700000_39.8.1420.15","doi-asserted-by":"publisher","DOI":"10.1152\/jn.90843.2008"},{"key":"2023041803345700000_39.8.1420.16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-00715-0"},{"key":"2023041803345700000_39.8.1420.17","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00024.2005"},{"key":"2023041803345700000_39.8.1420.18","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00166.2014"},{"key":"2023041803345700000_39.8.1420.19","unstructured":"Computational Memory Lab of University of Pennsylvania (2008) EEG Toolbox. 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