Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?

Abstract

Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.\u3cbr/\u3e