Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena
Aditya Nanda,
Graham W. Johnson,
Yu Mu,
Misha B. Ahrens,
Catie Chang,
Dario J. Englot,
Michael Breakspear,
Mikail Rubinov
Affiliations
Aditya Nanda
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Corresponding author
Graham W. Johnson
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
Yu Mu
Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
Misha B. Ahrens
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
Catie Chang
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
Dario J. Englot
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
Michael Breakspear
School of Psychology, University of Newcastle, Callaghan, NSW 2308, Australia; School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
Mikail Rubinov
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA; Corresponding author
Summary: Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.
