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Recent advances in high\u2010resolution anatomical modeling enable surface\u2010based analysis of cerebellar activity. At the same time, MEG technology has evolved, with on\u2010scalp systems employing optically pumped magnetometers (OPMs) emerging as an alternative for conventional superconducting quantum interference device (SQUID)\u2010based systems. In contrast to rigid one\u2010size\u2010fits\u2010all SQUID sensor helmets, OPMs allow flexible positioning of the sensors on the participant's scalp to provide improved coverage of the cerebellum. To assess the benefits provided by OPMs in detecting cerebellar activity, we conducted simulations using a high\u2010resolution model of the human cerebellum, where we compared OPM arrays consisting either of single\u2010axis or triaxial sensors to commercial SQUID sensor arrays. We show that both OPM types measure stronger net signals from across the cerebellum compared to the SQUID\u2010based systems. OPMs also reduce signal correlations between the cerebral and cerebellar cortices, improving source separability. Increasing the number of OPM sensors leads to larger gains in total information capacity compared to SQUIDs. In all metrics, triaxial OPMs outperformed single\u2010axis configurations. These results suggest that already a 102\u2010sensor, triaxial OPM\u2010based on\u2010scalp MEG system could substantially improve noninvasive electrophysiological studies of the human cerebellum.<\/jats:p>","DOI":"10.1002\/hbm.70514","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:36:53Z","timestamp":1774586213000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficacy of Optically Pumped Magnetometers in Detecting Activity From the Cerebellar Cortex"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0896-0340","authenticated-orcid":false,"given":"Santtu","family":"Roos","sequence":"first","affiliation":[{"name":"Department of Neuroscience and Biomedical Engineering Aalto University  Espoo Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matti","family":"H\u00e4m\u00e4l\u00e4inen","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Biomedical Engineering Aalto University  Espoo Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joonas","family":"Iivanainen","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Biomedical Engineering Aalto University  Espoo Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20851"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cne.903260405"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116817"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2008.01.003"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nn.4504"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116192"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.053431"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119747"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1973-0348991-3"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119027"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118025"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nphys566"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12311-015-0739-8"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.26490"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1101\/lm.59603"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1053\u20108119(03)00366\u20105"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.161.3843.784"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/83.623193"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.021"},{"key":"e_1_2_10_21_1","first-page":"267","article-title":"MEG and EEG data analysis with MNE\u2010Python","volume":"7","author":"Gramfort A.","year":"2013","journal-title":"Frontiers in Neuroinformatics"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.10.027"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.25645"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.01.082"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2411459121"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.65.413"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/10.16463"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.3389\/neuro.09.031.2009"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.06.004"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1972-24.2025"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118747"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.12.048"},{"key":"e_1_2_10_33_1","unstructured":"Jacobson A.2024.\u201cgptoolbox: Geometry Processing Toolbox.\u201dhttp:\/\/github.com\/alecjacobson\/gptoolbox."},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-0581-1_141"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511846380.002"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12311-013-0511-x"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1705414114"},{"key":"e_1_2_10_38_1","unstructured":"Larson E.2024.\u201cMne\u2010Python.\u201dZenodo. 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