Extended Data Fig. 1: Flow diagram and validation of the CBASS method.

CBASS links power increases in a frequency band during a particular state to events in the temporal domain. Here we look for events responsible for the power increase in the γ range (30–80 Hz) in mouse V1 cortex during locomotion. a: CBASS uses multichannel time series (black) where the state of interest is indexed (i.e. locomotion, purple). b: The signal is band-pass filtered in the γ range. Candidate events (gray bars) are taken at the trough of the filtered signal in an arbitrary reference channel (red). Here the reference channel is taken as the closest to Layer 4. c: Spectrotemporal dynamics at the time of candidate events are parameterized using the real and imaginary part of the analytical representation (matlab function hilbert) of the filtered LFP in each channel (Supplementary Methods). d: Three dimensional UMAP embedding showing the cloud of candidate events in the parametric space. Events occurring during locomotion (yellow) are present in all regions of the cloud. e: CBASS estimates whether specific spectrotemporal profiles (i.e. regions of the cloud) occur preferentially during locomotion. The cloud is partitioned randomly, and a binomial test is performed in each partition to test if the occurrence of locomotion is higher than overall. This operation is repeated many times (n = 1000). f: A score is derived for each candidate event as the fraction of time it fell into an enriched partition. This score is stronger in regions of the cloud (i.e. event profiles) associated with locomotion. g: CBASS finds the threshold of the enrichment score, maximizing the centroid distance between the cloud of retained and unretained events. h: Retained events (orange) superimposed on the raw data from panel a. i: The validity of the thresholding is tested on surrogate data having the same size, spectral power and covariance matrix across channels as the original data. This data is obtained by decomposing the transform and remixing them (Supplementary Methods). Upper: excerpt of the real (left) and surrogate (right) LFP. Lower: spectral power within (gray) and across channels (orange dotted line) for the real (left) and surrogate LFP (right). j: Distribution of enrichment score for candidate events in real (left) and surrogate (right) data in an example recording. k: Fraction of candidate events above threshold for surrogate data for 200 recording sessions over 19 mice. No candidate event passes threshold for most surrogate sessions. l: Illustration of the methodology used to estimate the laminar position of LFP channels across cortical layers. The average current source density (CSD) of the response to a high-contrast drifting grating stimulus consists of a primary sink in cortical layer four (purple) and a secondary sink occurring at longer latencies in layer 5b (red). This permits a 2-point alignment of a layer boundaries template estimated from histological atlas data. m: Average field potential around γ events (upper), associated CSD activity (middle), and power spectrum (lower) of the average event field (orange) (n = 19 mice). n: Same as m, when choosing the deepest channel as reference (n = 19 mice). o: Upper: average lag between γ events identified with a L4 and a deep reference channel. Lower: cumulative distribution of the lag between γ events identified with a L4 and a deep reference channel. Changing reference channel results in an overall phase shift in retained events (n = 200 session; 19 mice). p: Same as m, when retaining a matching number of events solely based on amplitude across channels. q: Events retained based solely on amplitude have only an 80.60 ± 0.51% overlap coefficient with events retained by CBASS (n = 200 session; 19 mice). Error Bars: s.e.m; shaded areas: mean ± s.e.m. See Supplementary Table 1 for detailed statistics and Supplementary Table 2 for statistical samples.