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. 2024 Jan 18;27(3):547–560. doi: 10.1038/s41593-023-01554-7

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 7 — We examined how robust the spectrolaminar pattern was to several recording quality metrics such as the number of single units detected and whether units on a probe had visual responses above baseline firing rate. We split our data from Study 1 into probes with zero units (n = 20), probes with low number of units (0 > number of units > 13, n = 148) and many units (>= 13 units, n = 156). These three subpopulations were examined for percentage identifiable crossover and goodness of fit value. Based on the FLIP algorithm, 17 (85%) of zero-unit probes had an identifiable crossover, 111 (75%) low unit probes had an identifiable crossover, and 120 (77%) of many unit probes had an identifiable crossover, and these differences in proportion were not significantly different (chi-square test, P > 0.05). (a) Goodness of Fit values for the population of probes recordings for which the number of isolated single units was zero (left), low (< 13, middle panel), and high (≥ 13 units, right panel). Absolute goodness of fit value distributions were not statistically different between subpopulations (zero unit probes 0.8302 mean +/ 0.4169 SD; low unit probes 0.8755 mean +/ 0.3201 SD; many unit probes 0.8517 mean +/ 0.3668; P(zero vs low) = 0.6014, CI = −0.2174 to 0.1268, t-stat = −0.5212, df = 126; P(zero vs many) = 0.8239 CI = −0.2128 to 0.1696, t-stat = −0.2229, df = 135; P(low vs many) = 0.6014 CI = −0.1133 to 0.0658, t-stat = −0.5231, df = 229; two-sample t-test). (b) Mean spectrolaminar maps for the different sub-populations of probes. The Image Similarity values were similar between subpopulations IS(zero vs low) = 0.5802; IS(zero vs many) = 0.5375; IS(low vs many) = 0.7603). We split our data from Study 1 into probe recordings where the number of stimulus-responsive units was zero (n = 41), low (0 < responsive units <= 7, n = 143), and high (> 7 responsive units, n = 140). Again, we found that the spectrolaminar pattern was present in about the same proportion across these groups. Based on the FLIP algorithm, 36 (88%) of zero responsive unit probes had an identifiable crossover, 109 (76%) low responsive unit probes had an identifiable crossover, and 103 (74%) of many responsive unit probes had an identifiable crossover (chi-square test, P > 0.05). (c) Goodness of Fit values for the population of probes where the number of stimulus-responsive units was zero (left panel), low (middle panel), and high (right). Absolute goodness of fit value distributions were not statistically different between subpopulations (P(zero vs low) = 0.1415; P(zero vs many) = 0.6969; P(low vs many) = 0.1598; unpaired t-tests). (d) Mean relative power maps were similar between subpopulations: IS(zero vs low) = 0.7411; IS(zero vs many) = 0.6112; IS(low vs many) = 0.8071. (e) Relationship between quality of CSD maps and single unit presence. Mean CSD maps across probe recordings where the number of single units was zero (left), low (middle), or high (right). The three probe groups had qualitatively similar patterns of sinks/sources.