Abstract
The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). MsCr measures reflect the behavior of conventional criticality measures (such as the branching parameter) across temporal scale. We applied MsCr to MEG and EEG data in several telling degraded information processing scenarios. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity: MsCr fingerprints showed deviance in the four states of compromised information processing examined in this study, disorders of consciousness, mild cognitive impairment, schizophrenia and even during pre-ictal activity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.
Highlights
The brain is universally regarded as a system for processing information
We propose a family of measures and apply them, as a proof of concept, to EEG and MEG datasets in four scenarios: disorders of consciousness (DOC), mild cognitive impairment (MCI), schizophrenia, and in the pre-ictal state
Σ: neural gain parameter γ: The exponent of the power law fit to the avalanche average size per duration distribution α: The exponent of the power law fit to the avalanche size distribution δ: The exponent of the power law fit to inter avalanche interval distribution τ: The exponent of the power law fit to the avalanche duration distribution a, b: the slope and intercept of the linear fit to a given exponent time dependent curve, e.g. aσ, α would be the slope of α( t) plotted against σ ( t)
Summary
The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Reduced capacity for information processing will be expressed as deviance from this requisite multiscale character of spatiotemporal activity This deviance is captured by a general family of multiscale criticality measures (MsCr). Activity will possess just the right admixture of a multitude of small-scale fast activity (resulting from local 1st order processing) together with much less frequent increasingly slower and more wide-spread events (resulting from higher order orchestration), in effect matching the hierarchical delay structure embedded in neural network organization. Deviance from this optimal organization will gradually disrupt information processing until the point it altogether vanishes. Σ: neural gain parameter (branching parameter) γ: The exponent of the power law fit to the avalanche average size per duration distribution α: The exponent of the power law fit to the avalanche size distribution δ: The exponent of the power law fit to inter avalanche interval distribution τ: The exponent of the power law fit to the avalanche duration distribution a, b: the slope and intercept of the linear fit to a given exponent (or pair of such) time dependent curve, e.g. aσ , α would be the slope of α( t) plotted against σ ( t)
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