The architecture of functional brain network modulated by driving during adverse weather conditions

Traffic accidents caused by adverse weather conditions have attracted the attention of many countries. Previous studies have focused on the driver’s response in a particular situation under foggy conditions, but little is known about the functional brain network (FBN) topology that is modulated by driving in foggy weather, especially when the vehicle encounters cars in the opposite lane. An experiment consisting of two driving tasks is designed and conducted using sixteen participants. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase-locking value (PLV). Based on this, a PLV-weighted network is subsequently generated. The clustering coefficient (C) and the characteristic path length (L) are adopted as measures for the graph analysis. Statistical analyses are performed on graph-derived metrics. The major finding is that the PLV is significantly increased in the delta, theta and beta frequency bands while driving in foggy weather. Additionally, for the brain network topology metric, compared with driving in clear weather, significant increases are observed (driving in foggy weather) in the clustering coefficient for alpha and beta frequency bands and the characteristic path length for all frequency bands considered in this work. Driving in foggy weather would regulate FBN reorganization in different frequency bands. Our findings also suggest that the effects of adverse weather conditions on functional brain networks with a trend toward a more economic but less efficient architecture. Graph theory analysis may be a beneficial tool to further understand the neural mechanisms of driving in adverse weather conditions, which in turn may help to reduce the occurrence of road traffic accidents to some extent.