@article{Suppression:1949,
      recid = {1949},
      author = {Kardan, Omid},
      title = {No Hurst for the Weary: Suppression of Scale-Free Brain  Activity as a Measure of Cognitive Effort and Predictor of  Working Memory Performance},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2019-08},
      pages = {85},
      abstract = {Since its proposition about 2 decades ago, the theory of  assessing the brain as a neural network with self-organized  criticality has triggered a multitude of research due to  its conceptual appeal. Scale-free brain activity as  measured by the Hurst exponent (H) of electrophysiological  signals and fMRI BOLD signals has been a hallmark of  queries related to the ‘criticality hypothesis’ within the  field of cognitive neuroscience, which models brain at rest  as a network configured to operate near a critical state.  In this dissertation I investigated the significance of H  in EEG and fMRI data with regards to cognitive processes  involved in working memory and learning. In chapter 1, I  utilized global H suppression in EEG to distinguish working  memory load from cognitive effort. Results from two visual  working memory experiments with varying memory set size  provided evidence for the suppression of scale-invariance  in EEG due to task difficulty that continues even after  working memory capacity has been reached. In contrast, task  performance and oscillatory signals of working memory load  both plateau beyond working memory capacity. This suggests  that H suppression may be used to reliably indicate an  effortful state. In chapter 2, I used H measured with fMRI  data to predict learning in a dual n-back (DNB) working  memory task. I hypothesized that learning potential is  higher when brain networks are poised closer to a critical  state, and thus higher H can be used to predict more  learning and improvement on cognitive tasks. The results  show that higher H during learning distinguished task  improvers from non-improvers. As a comparison, neither  baseline task performance nor fMRI functional connectivity  strength reliably classified improvers vs. non-improvers. I  then successfully cross-validated the H-based model from  the DNB dataset on an independent fMRI dataset of  participants performing a completely different working  memory task (word completion). Taken together, these  results suggest that scale-free brain activity can be used  as an objective measure of an individual’s cognitive state  and provide support for the utility of the criticality  hypothesis.},
      url = {http://knowledge.uchicago.edu/record/1949},
      doi = {https://doi.org/10.6082/uchicago.1949},
}