Scale-invariant Analyses Of Critical State In Whole-brain Of Zebrafish

There are various advantages,in experiments and stimulation,in information processing like maximum mnemonic repertoire size,information diversity and dynamic range.Previous studies had shown limited scale-invariant criticality because of the restriction of techniques and experimental animals.Low spatial resolution of scale-invariant criticality was observed in the whole brain of humans using functional magnetic resonance imaging(fMRI)and magnetoencephalography(MEG),in brain regions of humans and rodents using electrocorticography(ECoG)and voltage imaging separately and in a dot of brain of rodents,cats and nonhuman primates using the local field potential(LFP).Additionally,high spatial resolution,the neuronal group level,of that was observed in rodents using 2-photon imaging,but only in tens neurons.In general,there was not a direct and complete evidence in a kind of animal to prove the hypothesis that the criticality of the brain is scale-invariant from the neuronal group level to the whole brain.To support the hypothesis,we recorded neuronal activity from every neuron of the whole brain in zebrafish using 2-photon imaging.So,in this study,we demonstrate wider scale-invariant criticality from two points: first,scale-invariant criticality in four-scale pellets,composed of many neurons,from tens of pellets to tens of thousands of pellets in the whole brain;second,Scale-invariant criticality in four-scale windows of the whole brain from tens of neurons to tens of thousands of neurons.It was the first time that proved the criticality of the brain is scale-invariant across four orders of magnitude in multidimensional and multiple point of view on a kind of animal.Moreover,we demonstrated the reliability of results in quantification by three methods and two models;suggested the two dimensions of criticality about the brainin sizes and lifetimes by frame and matched shuffle;and outstood the uniformity of scale-invariance criticality in pellets compared with scale variance of other features.These would help us,with a multidimensional point,to simulation the brain or complex networks and to understand the secret of intelligence or disease about brains.