Information integration in large brain networks
Fig 4
A We split the available ECoG electrodes in two macaque monkeys into overlapping sets of 14 electrodes. The ground-truth MIB of 14 electrodes can be identified through a brute-force search, and compared to the spectral partition estimated from the correlation matrix of data from those electrodes. Here, we subtracted ΦG (normalized) across the ground-truth MIB from ΦG (normalized) across the spectral partition. There was a difference of 0 bits for 67/112 (mean difference = 0.0002 bits) datasets from George’s brain, and a difference of 0 bits in 46/112 (mean difference = 0.0001 bits) datasets from Chibi’s brain. Red squares indicate the mean difference in ΦG (normalized) across all datasets from one brain, and blue bars indicate standard error of the mean. B Spectral clustering found the exact MIB for the same 67/112 datasets in George’s brain (mean Rand Index = 0.87) and 46/112 datasets in Chibi’s brain (mean Rand Index = 0.79). C We used our spectral clustering approach to estimate the MIB of Chibi’s entire left cortex, and found that it split posterior sensory areas from anterior association areas. Electrodes are colored according to the community in which they are clustered; the electrodes that were excluded from the analysis because they displayed consistent artifacts are colored grey. D ΦG (normalized) across the spectral partition of Chibi’s left cortex (solid green line) was lower than it was across 4/6 partitions identified by the Replica Exchange Markov Chain Monte Carlo (REMCMC) method (yellow dashed lines) [21]. The other 2/6 partitions yielded values of normalized integrated information that were very slightly lower (0.0002 bits) than the value across the spectral clustering-based partition, and were dissimilar both to each other (Rand Index = 0.5) and to the spectral partition (Rand Indices = 0.5, 0.55), suggesting that there were several local minima of normalized integrated information in Chibi’s brain. We ran the REMCMC algorithm for 10 days. E Our estimate of the MIB of George’s left cortex using spectral clustering also (largely) split posterior sensory areas from anterior association areas. F ΦG (normalized) across the spectral partition of George’s left cortex was lower than it was across all bipartitions identified by the REMCMC method. Note the difference in scale on the x-axes of D and F; it is unclear why this scale should differ between the two brains.