Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer’s disease

Abstract

Investigating the early Alzheimer’s disease (AD) more emphasizes sensitive and specific biomarkers, which can help the clinicians to monitor the progression and treatments of AD. Among these biomarkers, default mode network (DMN) functional connectivity is gaining more attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. However, besides changed functional connectivity of DMN, other functional networks haven’t yet been examined systematically. Recent brain imaging studies reported that a number of reproducible and robust functional networks, which were distributed in distant neuroanatomic areas. Inspired by these works, in this paper, we apply sparse representation to the whole brain signals to identify these reproducible networks and detect partly affected brain regions of Alzheimer’s disease, then adopt sparse inverse covariance estimation (SICE) approach to investigate the changed functional connectivity of intrinsic connectivity networks. Our experimental results show that besides DMN, AD is also affected by others large scale functional brain networks and regions, e.g., executive control network (ECN), frontoparietal network (FPN), where in the superior frontal gyrus (SFGmed) and middle frontal gyrus (MFG) of ECN and in the part paracentral Lobule (PCL) of FPN have an increased functional connectivity, as well as in the Superior Parietal Gyrus (SPG) regions of FPN has shown decreased connectivity. The results may suggest AD is associated with larger scale functional networks and causes the functional connectivity change of many different brain regions. It also proves that these networks may sometimes work together to perform tasks, and such changed functional connectivity may provide a useful baseline for early AD diagnosis.