The brain at criticality: variability of brain spontaneous activity and relevance to brain functions

  • Mianxin Liu

Student thesis: Doctoral Thesis


The brain activities are characterized by spontaneous and persistent irregular fluctuations in space and time. Criticality theory from statistical physics has been proposed as a principle to explain the variability in normal brain spontaneous activity and has suggested the functional benefits of variability, such as maximized dynamic range of response to stimuli and information capacity. In parallel, the brains show variability in other aspects, such as the structural heterogeneity across brain regions, the intra-individual variability across experimental trials, and the behavior difference across groups and individuals. The associations between the variability of spontaneous activities and these different types of structural, intra and inter-individual variabilities remain elusive. My doctoral study thus aimed to bridge the brain variability and the above-mentioned variations based on criticality theory and analysis of empirical data. As a preparatory analysis, we first collected evidence to prove criticality in human functional magnetic resonance imaging (fMRI) data. The advanced statistical criteria were used to exclude potential artefacts that can induce power-law scaling without the mechanism of criticality. In the first part of the study, we addressed methodological issue and tested whether several measures of either spatial or temporal complexity due to experimental limitations could be reliable proxy of spatiotemporal variability (related to criticality) in vivo. The high spatiotemporal resolutions of whole-cortex optical voltage imaging in mice brain during the waking up from anesthesia enabled simultaneous investigation of functional connectivity (FC), Multi-Scale Entropy (MSE, measure of temporal variability), Regional Entropy (RE, quantity of spatiotemporal variability) and the interdependency among them under different brain states. The results suggested that MSE and FC could be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. This study also lays methodological basis for the third study in this thesis. In the second study, we explored the interaction between spontaneous activity and evoked activity from mice brain imaging under whisker stimulus. The whisker stimulus will first evoke the local activation in sensory cortex and then trigger whole-cortex activity with variable patterns in different experimental trials. This trial-to-trial variability in the cortical evoked component was then attributed to the changes of ongoing activity state at stimulus onset. The study links ongoing activity variability and evoked activity variability, which further consolidates the association between ongoing activity and brain functions. In the third study, we measured the signal variability of the whole brain from resting state fMRI, and developed the multivariate pattern of cortical entropy, called entropy profile, as reliable and interpretable biomarker of individual difference in cognitive ability. We showed that the whole cortical entropy profile from resting- state fMRI is a robust personalized measure. We tested the predictive power for general and specific cognitive abilities based on cortical entropy profiles with out- of-sample prediction. Furthermore, we revealed the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. This study provides new potential biomarker based on brain spontaneous variability which could benefit the applications in psychology and psychiatry studies. The whole study laid a foundation for brain criticality-/variability-based studies and applications and broadened our understanding of the associations between neural structures, functional dynamics and cognitive ability

Date of Award26 Aug 2020
Original languageEnglish
SupervisorChangsong ZHOU (Supervisor)

User-Defined Keywords

  • Brain
  • Magnetic resonance imaging
  • Cognitive neuroscience

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