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. 2017 Sep 1:8:633.
doi: 10.3389/fphys.2017.00633. eCollection 2017.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Travis J Wiltshire  1   2 , Matthew J Euler  1 , Ty L McKinney  1 , Jonathan E Butner  1
Affiliations

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Travis J Wiltshire et al. Front Physiol. .

Abstract

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Keywords: coordination; dimensionality; dynamical systems; fractal scaling; repetition priming; self-organization; soft-assembly.

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Figures

Figure 1
Figure 1
Event related potentials demonstrating repetition suppression effect and scalp topography. ERPs based on the average of channels PO7 and PO8, for each exposure (1–6) to the stimuli, are depicted in the left panel. The dotted lines indicate (from left to right) the onset of the stimulus and the peak amplitude of the grand-average ERP across exposures, corresponding to the P1 component. The right panels depict topographic plots for pre- (top right) and post-stimulus (bottom right) activity, with the stars indicating the electrodes that were averaged to obtain the ERPs depicted in the left panel. The post stimulus scalp map depicts the activity 136 ms post stimulus, corresponding to the peak amplitude in the grand average ERP.
Figure 2
Figure 2
Group-averaged correlation dimension scalp maps. The maps show the estimated correlation dimension (D2) across increasing levels of familiarity (left to right) prior to (top) and following (bottom) stimulus onset. Exp = exposure.
Figure 3
Figure 3
Predicted correlation dimension values. The figure shows the predicted Correlation Dimension (D2) values across increasing levels of familiarity (left to right along the x axis) and segmented by pre- (red triangles) and post-stimulus (blue circles).
Figure 4
Figure 4
Group-averaged scaling exponent scalp maps. The maps show the estimated scaling exponents (α) across increasing levels of familiarity (left to right) prior to (top) and following (bottom) stimulus onset. Exp = exposure.
Figure 5
Figure 5
Predicted scaling exponent values. The figure shows the predicted scaling exponents (α) across increasing levels of familiarity (left to right along the x axis) and segmented by pre- (red triangles) and post-stimulus (blue circles).
Figure 6
Figure 6
Group-averaged correlation dimension and scaling exponents scalp maps comparing rest to pre- to post-stimulus. The maps show the estimated correlation dimension (top) and scaling exponents (bottom) as a function of resting conditions (left), prior to (middle) and following (right) stimulus onset.
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