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 paper 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 grey 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, is a product of how these physical processes affect dynamic network communication.

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

“…For instance, computational modelling of oscillatory activity suggests that PAC may be a key component in balancing excitation-inhibition interactions (Peterson & Voytek, 2015). Voytek & Knight (2015) therefore proposed deficient PAC as an index for a local excitation/inhibition imbalance in various psychopathologies and in ASD in particular, which could be due to reduced inhibition (Vogels & Abbott, 2009) or an affected excitation-inhibition ratio (Rubenstein & Merzenich, 2003) as discussed in Section 2.…”

“…As outlined in Section 2, the synchronisation of specialised neural networks is key to normal sensory, perceptual and cognitive function. Disruption in neural synchrony or failures in dynamic network communication have been hailed as a unifying explanation for a wide range of behavioural and neurocognitive disorders and have been widely reviewed (Menon, 2011;Uhlhaas & Singer, 2006;Voytek & Knight, 2015). A key aspect of the development of these models has been the major advances in techniques for studying connectivity (see Hutchison et al, 2013).…”

“…2). Firstly, as described, local functioning will be affected as it is hard to achieve optimal signal-to-noise ratios (SNR) with a suboptimal balance between excitation and inhibition (Voytek & Knight, 2015); this will be reflected by reduced PAC . Such an imbalance may result in ASD-typical sensory hyper-sensitivities, because strong external stimuli generate strong incoming signals via pyramidal neurons that may result in excessive local activation if neural structures for effecting suppression are underdeveloped (e.g.…”

“…Voytek & Knight, 2015), especially in the context of local to global brain connectivity in ASD. Indeed, Khan et al, (2013) have recently employed a PAC metric to index local connectivity in relation to face stimuli in the fusiform face area (FFA).…”

Coherence: The Measurement and Application of Brain Connectivity

“…Abnormal patterns of brain connectivity have been linked to numerous developmental and psychiatric conditions, and reduced symptomatology in these conditions is dependent on the normalization of brain network connectivity (Voytek & Knight, 2015). For example, atypical patterns of connectivity have been found in individuals with schizophrenia (Su, Hsu, Lin & Lin, 2015), epilepsy (Widjaja et al, 2015), and Alzheimer's Disease (Qin et al, 2015), to name a few.…”