Abstract

From molecular mechanisms to global networks, variable brain fluctuations are the hallmark of neurodegeneration. RSNs can be understood as dynamical systems presenting time-dependent functional connectivity (FC) variations that influence brain function during health and disease (Breakspear, 2017; Sporns, 2014; Hutchison et al., 2013). Despite this highly variable environment, most RSN research on dementia only employs static FC (SFC) measures (i.e., averages of FC across the whole MR acquisition time) (Sporns, 2014). Also, the field has broadly favored linear correlation measures (e.g., Pearson's R), which are blind to non-linear connectivity interactions. These limitations may partly explain why standard SFC analyses have yielded inconsistent sensitivity and specificity indices (Pievani et al., 2014; Sedeño, 2017) in classifying between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patient groups (Pievani et al., 2014). The heterogeneous network fluctuations caused by neurodegeneration might not be captured by SFC and linear correlations, calling for non-linear FC methods and dynamical frameworks that outperform time-averaged connectivity (Liegeois et al., 2019). Here, we developed a Dynamic Connectivity Fluctuation Analysis (DCFA) which targets FC fluctuation across time and captures both linear and non-linear signal modulations (Fig. 1). We tested this framework's accuracy and generalizability to discriminate among healthy controls and two dementia subtypes (AD and bvFTD), based on 300 subjects (from three international dementia centers and online databases).