Abstract
An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.
Highlights
The presence or absence of a task is accompanied by increases in variability across different scales including neuronal firing rates changes in field potentials[19,20], variation in fMRI blood oxygen level dependent (BOLD signal)[21], and in EEG frequency bands[22]
Is there a way to identify the shared neural architecture underlying the cognitive processes associated with rest and active states while quantifying how these processes diverge from that shared architecture of neural activity? In this paper, we applied mathematical methods analogous to those of quantum mechanics, and the concept of phase space to EEG recorded during rest and movie-watching to extract spatial and transitional properties of dynamic neural activity
An analogous uncertainty relationship to that of quantum mechanics was established, with the full mathematical derivation described in the methods
Summary
The presence or absence of a task is accompanied by increases in variability across different scales including neuronal firing rates changes in field potentials[19,20], variation in fMRI blood oxygen level dependent (BOLD signal)[21], and in EEG frequency bands[22]. Through transcranial direct current stimulation (tDCS) it has been shown that frontal-lobe stimulation increases one’s proclivity to mind wander[23,24] These differences are associated with changes in properties of neural activity but not in changes in the underlying neural architecture. Quantum systems (in the Schrodinger formulation of quantum mechanics) are described by wavefunctions which square to a probability distribution leading to the loss of local determinism and the Heisenberg uncertainty principle (for an overview/intro to the subject s ee[25]). The mathematical methods of quantum mechanics are applied to EEG data to extract a proxy to phase space This quasi-quantum approach naturally generates the concepts of ‘average’ position, ‘average’ momentum and culminates in an analogous Heisenberg uncertainty principle. Does devising this model probe questions into the functions of the brain, but it provides a novel approach to analysing the myriad of data available in neuroscience
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