Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease

Abstract

Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this article, we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low-frequency (<80 Hz) oscillations, allowing for brief windows of communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders-including Parkinson's disease, autism, depression, schizophrenia, and anxiety-are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural gray or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states or their treatment are a product of how these physical processes affect dynamic network communication.

Keywords: Anxiety; Autism; Coherence; Coupling; Depression; Gamma; Network dynamics; Neural oscillations; Parkinson’s disease; Schizophrenia; Theta.

Fig. 1. Example power spectral changes

All four plots contain an exemplar PSD consisting of a 1/f process plus an oscillation centered around 12 Hz (solid black lines). This PSD is then modified in four different ways (dashed colored lines). (A) In this example, the PSD in black has been modified by simple translation, adding power at all frequencies (blue line). (B) Here the background 1/f process remains unaffected, all that has changed is that the 12 Hz oscillation has been reduced in power (red line). (C) A single manipulation—rotation of the PSD about a pivot frequency (40 Hz)—results in a simultaneous decrease in low frequency power and an increase in high frequency power (purple line). (D) Here two separate effects—translation of the PSD and reduction of 12 Hz oscillatory power—has a similar effect as the single rotation process described in (C) in that low frequency power is reduced and high frequency power is increased. (c.f., Miller et al., 2013 (21))

Fig. 2. Evidence for spectral slope changes

(A) During sleep the human subdural ECoG PSD has a steep negative slope that flattens (whitens) during wakefulness. (B) Simulations of human ECoG PSD suggest that PSD slope changes arise as a function of the dendritic response to an input. As the strength of the input increases, more neurons fire simultaneously, forcing a greater local population to become refractory within a shorter time window, increasing the rise time. Thus, for weaker inputs with greater rise times the slope of the PSD is flatter (blue), whereas for stronger inputs with smaller rise times the slope of the PSD is steeper (red). Thus, within our framework, if the dendritic response is driven by ephaptic coupling, the stronger the coupling, the steeper the slope; with weaker ephaptic coupling, spike times are less temporally correlated and the slope is flatter. (From Freeman & Zhai, 2008)(68)