band imaging data, which appeared superior to the other acquisitions in terms of both accuracy and reliability of rfMRI (Zuo and Xing, 2014). More de- tails on these data are publicly accessible via the FCP/INDI website (http: //fcon_1000.projects.nitrc.org/indi/enhanced/index.html) . All image data were preprocessed using the Connectome Computation System (CCS) pipeline. The preprocessing strategy included discarding the first several vol- umes (10 seconds), removing and interpolating spikes that arise from either hardware instability or head motion, slice-time correction, image intensity nor- malization, and removing the effect of physiological noise by regressing out twenty-four parameters from a motion model (Yan et al., 2013; Satterthwaite et al., 2013) as well as nuisance variables such as white matter and cerebrospinal fluid signals, along with both linear and quadratic trends. Details of the image preprocessing steps are described in Xu et al. (2015). In total, we processed data from 418 individual participants. The quality control procedure in the CCS ex- cluded 19 participants due to their low-quality multimodal imaging datasets, which met at least one of the following criteria: (1) failed visual inspection of anatomical images and surfaces; (2) mean frame-wise displacement > 0.2 mm; (3) maximum translation > 3 mm; (4) maximum rotation > 3 $^{◦}$; or (5) minimum cost of boundary-based registration (a measure of image registration quality) > 0.6. Additionally, 51 participants were excluded from subsequent analyses because of clinical diagnoses as defined by DSM-IV or ICD10 and 32 due to incompleteness of the multimodal imaging datasets. This leads to a lifespan sample of 316 healthy participants. Within this sample we focus on a sub-sample of healthy adults aged 18-30 years, comprising 80 participants. We term the functional connectivity network estimated over the duration of the entire scan session as the static functional connectivity (sFC). We con- Static functional connectivity 5