A computational model called COALIA simulates human cortical micro-circuits, including specific neuron types and thalamo-cortical regulation of cortico-cortical connectivity. The model generates EEG that matches brain rhythms recorded in humans during wakefulness and sleep. It reproduces disynaptic disinhibition of basket cells and pyramidal neurons via long-range activation of VIP interneurons. The model predicts that thalamic output strength and dynamics control local and long-range cortical information processing. It also reproduces and explains clinical TMS-evoked EEG complexity in disorders of consciousness patients and healthy volunteers through modulation of thalamo-cortical connectivity governing cortico-cortical communication.
Information-based metrics of neural activity can help quantify consciousness before and shortly after birth. Using fetal magnetoencephalography (fMEG) in human fetuses and neonates, researchers evaluated measures of entropy, compressibility, and fractality. Lempel-Ziv-Complexity (LZC) was the most practical metric because it is unequivocal and requires low computational effort, whereas fractality and entropy measures need more parameter adjustments. Comparing a brain-activity channel with a control channel in neonates showed significant differences in most complexity metrics, serving as proof of concept. For fetal data, results were less clear, possibly due to leftover maternal signals. The inconsistency across metrics highlights challenges in using complexity metrics as neural correlates of consciousness.