FIG. 1.
Infra-slow spectral clustering algorithm. (A) i, Gamma band-limited power (BLP) is low-pass filtered at <0.1 Hz (red). Nonoverlapping, 300 sec segments are created (blue dividing lines) ii, Correlation coefficients for each segment between all electrodes are compared with surrogate time series, and significant interactions between channels are represented in a matrix here denoted R. iii, This is converted to a mutual nearest-neighbor adjacency matrix W with the number of neighbors (knn=2). iv, The normalized spectral clustering algorithm is applied as described in the “Materials and Methods” section. An abbreviated plot of generated eigennumbers illustrates the “gap heuristic” used to select the number of clusters used, and the k-means algorithm is used to identify electrode clusters (on x-axis). v, An adjacency matrix denoted M is used to represent clustering of electrodes for one time segment. (B) A cumulative sum of M across all segments is used to generate a final clustering as shown for subject S5. Electrocortical stimulation (ECS) mapping of hand (yellow lines) and leg (red lines) motor areas is shown on this template brain, along with clusters that correspond (light blue, yellow electrodes). (C) Left: The cumulative sum of M values over time that is used for the final clustering. Right: The nearest-neighbors matrix used for final clustering. The algorithm results are denoted with each cluster represented by a colored rectangle corresponding to the electrode colors in (C).